Apparatus and method for providing environmental predictive indicators to emergency response managers

ABSTRACT

A method of predicting weather-exacerbated threats, said method comprising inputting localized weather measurement data into a weather threat prediction system; predicting future localized weather conditions based on said localized weather measurement data combined with modeling from National Weather Service Data; inputting natural environment and infrastructure data into said weather threat prediction system; correlating said infrastructure data with said predicted future localized weather conditions; and determining a threat level index over a region, a threat level indicating an area having a certain probabilistic likelihood of being harmed by said future weather conditions.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of and priority to U.S. ProvisionalPatent Application Ser. No. 61/114,357, filed Nov. 13, 2008, entitledSYSTEM AND METHOD FOR PROVIDING ENVIRONMENTAL PREDICTIVE INDICATORS TOEMERGENCY RESPONSE MANAGERS, which application is hereby incorporated byreference to the extent permitted by law.

BACKGROUND OF INVENTION

1. Field of Invention

The present invention generally relates to systems and methods forproviding decision support predictive indicators that will createreal-time probability impact information for decision makers andlogistical managers when allocating resources and man power responsiveto infrastructure repair, emergency events and/or natural resourceissues, and more particularly to the impact of weather events oninfrastructure, natural resources and/or environmental conditions.

2. Background of Art

Emergency Response Management are often the task of government agencies,utilities and businesses who are charged with responding in many crisissituations. The Federal Government utilizes the Federal EmergencyManagement Agency (FEMA) to respond to major catastrophes, such ashurricanes, earthquakes, nuclear accidents, tornadoes, wildfires and thelike. On a more local level, utility companies tasked with providingessential services to consumers (power, water, gas, sewage) have createdinternal Emergency Management departments with the goal of respondingquickly to outages often caused by weather events. In private industry,businesses which handle chemicals and other possibly harmful materialsgenerally have created similar internal Emergency Management departmentswhich deal with chemical spills and leaks and other environmentalhazards.

However, often times such emergencies are created and/or exacerbated byweather conditions. Certainly, hurricanes and tornadoes are, in and ofthemselves, weather conditions which may cause tremendous destruction.Wildfires, chemicals released by leaks or spills, and radioactive wastereleased in nuclear accidents are all subject to be worsened by wind.Downed power lines are caused by high winds and flying/falling debris,such as tree limbs, or by ice storms. Floods are generally caused byhigh rainfall amounts. Thus, the weather conditions during theseaccidents and disasters are responsible for much of the associateddanger and service outages.

Weather prediction should be an integral part of Emergency ResponseManagement—knowing the present and future weather conditions can helpEmergency Response Management to send resources not only to locationsthat are in need. However, current predictive techniques for weatherpatterns are generally performed on a national, or multi-state regionalscale. This large scale weather prediction is largely insufficient forpredicting the location of specific weather conditions with enoughprecision to ultimately assist Emergency Response Management personnel.Prior art weather monitoring stations are generally spaced 100-200 milesapart due to their expense and complexity. The wide spacing and skywardfocus of these monitoring stations largely prevents them from monitoringground conditions, and provides weather data accurate enough only topredict general weather patterns.

Many kinds of threats that the utility industry, natural resourcemanagers, emergency responders and government agencies face are createdand/or exacerbated by weather conditions. In particular threats createdby high winds, icing, and lightning strikes are among the most difficultto predict and assess, because of their highly time-dependent geospatialdistribution. The threat posed by the dispersion of chemicals releasedby leaks or spills, and radioactive waste released in nuclear accidentsis also difficult to predict and assess, because of the highly time andspace dependent weather conditions. Floods are generally caused by highrainfall amounts. Thus, the highly variable spatial and temporalvariability of weather conditions play a crucial role in how the utilityindustry and government agencies respond.

The utility industry knows that weather plays in the long-termmanagement of resources and has used long-term weather forecasts to planthe distribution of resources. However, the use of high spatial andtemporal resolution short-term forecasts pin-pointed at specific regionshas not been explored by either the utility industry or the governmentagencies charged with responding to natural or man-made disasters.Current weather numerical weather forecasts are generally performed on anational, or multi-state regional scale. This large-scale weatherprediction is largely insufficient for predicting with enough precisionthe location and severity of weather conditions to provide actionableintelligence to the utility industry or emergency responders. Again, asnoted above, prior art weather monitoring stations are generally spaced100-200 miles apart due to their expense and complexity. The widespacing of these monitoring stations largely prevents them fromproviding weather data at the local or neighborhood level, which iscrucial for the utility industry and emergency responders.

The national or multi-state regional forecasts are insufficient for morelocalized weather prediction needed during an emergency. Detailedinformation about the character of the wind field over a neighborhoodcould be the difference between whether a school can be evacuated intime to avoid a poisonous chemical cloud, or whether it would be betterfor students to remain inside. Further, a difference of just a fewdegrees in temperature or a few miles per hour of wind speed over adistance of less than a mile could be the difference between a few powerlines being downed versus hundreds of thousands of people in a heavilypopulated area being without power.

Accordingly, embodiments of the present invention provide for a systemand method that monitors environmental conditions at locations spacedcloser together than prior art monitoring stations. Thus, the greaternumber of monitoring stations in a smaller area provide for a higherresolution of weather data, allowing for a more precise and accurateforecast of conditions including weather data closer to the ground. Whensuch weather data is correlated with relatively static infrastructuredata, the present invention allows for a threat level index to becreated which indicates the localities which are most likely to bethreatened by the exacerbation of an event by the weather. EmergencyResponse Management may then use the threat level index to determinewhere and when to martial personnel.

BRIEF SUMMARY OF THE INVENTION

The present invention generally relates to systems and methods forproviding decision support predictive indicators that will createreal-time, detailed geographical distributions of the probability of theimpact of weather events on infrastructure, natural resources and/orenvironmental conditions and more particularly to the impact of stormdamage due to severe weather events (high winds, icing, and lightningstrikes) to infrastructure, for example the utility industryinfrastructure, or other infrastructure. The present invention alsorelates to systems and methods for prediction and assessment ofair-borne pollen, agricultural pathogens, and hazardous materials for avariety of public and private agencies.

One or more of the embodiments of the present invention provide for asystem and method for correlating localized weather predictions withrelatively static infrastructure data. In a preferred embodiment, anetwork of monitoring stations input weather data into the EAS system.The network of monitoring stations can generally be referred to as alocal Mesonet. These monitoring stations are spaced closer together thanthe 100-200 mile spacing of current monitoring stations, and are adaptedto capture weather data more often than current monitoring stations,allowing for finer resolution of weather data. The monitoring stationsor sensor suites can be remotely located and stand-alone systems withwireless transmission capability. The monitoring stations of the localMesonet are also strategically placed based on certain parameters tooptimize the efficacy of the stations and their respective data. Theweather prediction system then utilizes this higher resolution weatherdata to predict the future weather conditions for an area, includingfuture near-ground level weather. The processed data from the Mesonet iscombined with weather prediction Mesoscale meteorological modelsoperating on National Weather Service Data to predict the short-termweather at the neighborhood scale. Given a high spatial and temporalresolution meteorological forecast is made, the system combines theweather predictions with infrastructure data and/or natural environmentdata of the area to create a threat level index and a graphical mappresentation of any threats. As will be understood by one skilled in theart, certain weather conditions combined with certain types ofinfrastructure can create emergency conditions. The threat level indexis a probabilistic tool that rates areas in which certain infrastructureis likely to encounter certain, possibly hazardous conditions, and/oroutages, which can result in a high probability of danger and/or serviceoutage periods.

One embodiment of the present invention is to leverage on a SupervisoryControl and Data Acquisition (SCADA) system of an electrical utilitycompany. Many large electrical utility companies utilize a SCADA systemto monitor and relay information related line status and otherinfrastructure status. The SCADA system utilized by electrical utilitiesusually utilize a series of substations that are distributed throughoutthe electrical utilities line and infrastructure system. The substationsreceive communications from various monitoring devices that are activelymonitoring line status and the status of the other infrastructure. Thesubstations receive the information transmitted to it wirelessly or byhardwire from the various monitoring devices. The substation can packagethe data received and transmit the status to a central server via anethernet connection or other wide area network connection. The centralserver can process the information as well as transmit the informationto other systems. This SCADA system can utilize the power lines totransmit data information or other means. In one embodiment of thepresent invention the SCADA system utilized by an electrical utility canbe leveraged to transmit data from remote weather monitoring stationsfrom any location were there are existing power lines. Utilizing SCADAwill provide a comprehensive widespread network at low cost. The SCADAsystem can be equipped with electronic conversion devices that canwirelessly or via hardwire receive data transmission from remote weathermonitoring stations and convert the data readings to a format andprotocol that the SCADA system can handle and transmit. This embodimentis using the SCADA network available on the electrical power system in away that was not intended but is quite effective in leveraging the SCADAelectrical utility infrastructure to create a Local Mesonet network ofweather monitoring stations that is comprehensive providing a highdegree of spatial and temporal resolution that would be much moredifficult and less cost effective to achieve without SCADA.

Therefore, one embodiment of the present invention can be a system forpredicting weather-related threats, comprising an electrical utilityinfrastructure having a SCADA network adapted for monitoring andreporting infrastructure status including a plurality of substationsdispersed throughout a region serviced by the electrical utilityinfrastructure, where said substations including a transceiver moduleoperable to receive and transmit infrastructure status transmissions,and where said SCADA network includes a central server communicablylinked to said plurality of substations and adapted to receive andprocess infrastructure status transmissions transmitted from theplurality of substations. The embodiment can further include a localMesonet including a plurality of weather monitoring stations dispersedthroughout the region serviced by the electrical utility infrastructure,where each weather monitoring station including weather conditionsensors and transmitters adapted to transmit to the SCADA network datarepresentative of the weather conditions sensed by the weather conditionsensors. The central server can be communicably linked to a wide areanetwork and adapted to transmit said data representative of the weatherconditions over said wide area network. An electronic converter moduleintegral with the SCADA network can be operable to receive and convertthe data representative of the weather conditions and transmitted by theplurality of weather monitoring stations into a format and protocol thatcan be processed and transmitted by the SCADA network. An EAS computingsystem communicably linked to said wide area network and adapted toreceive data representative of the weather conditions as transmitted bythe central server can be adapted to present said data representative ofthe weather conditions to a user interface for user viewing.

In general SCADA refers to an industrial control system and is usually acomputer based system for monitoring and controlling a process orequipment or infrastructure. The process can be industrial,infrastructure or facility based. As discussed above infrastructureprocesses may be public or private, and include water treatment anddistribution, wastewater collection and treatment, oil and gaspipelines, the specific example above—electrical power transmission anddistribution, and large communication systems. A SCADA System canusually include the following subsystems: A Human-Machine Interface orHMI is the apparatus which presents process data to a human operator,and through this the human operator monitors and controls the process; Asupervisory (computer) system, gathering (acquiring) data on the processand sending commands (control) to the process; Remote Terminal Units(RTUs) connecting to sensors in the process, converting sensor signalsto digital data and sending digital data to the supervisory system.

The term SCADA usually refers to centralized systems which monitor andcontrol entire sites, or complexes of systems spread out over largeareas (anything between an industrial plant and a country). Most controlactions are performed automatically by remote terminal units (“RTUs”) orby programmable logic controllers (“PLCs”). Host control functions areusually restricted to basic overriding or supervisory levelintervention. For example, a PLC may control the flow of cooling waterthrough part of an industrial process, but the SCADA system may allowoperators to change the set points for the flow, and enable alarmconditions, such as loss of flow and high temperature, to be displayedand recorded. The feedback control loop passes through the RTU or PLC,while the SCADA system monitors the overall performance of the loop.

Data acquisition begins at the RTU or PLC level and includes meterreadings and equipment status reports that are communicated to SCADA asrequired. Data is then compiled and formatted in such a way that acontrol room operator using the HMI can make supervisory decisions toadjust or override normal RTU (PLC) controls. Data may also be fed to aHistorian, often built on a commodity Database Management System, toallow trending and other analytical auditing. SCADA systems typicallyimplement a distributed database, commonly referred to as a tagdatabase, which contains data elements called tags or points. A pointrepresents a single input or output value monitored or controlled by thesystem. Points can be either “hard” or “soft”. A hard point representsan actual input or output within the system, while a soft point resultsfrom logic and math operations applied to other points. (Mostimplementations conceptually remove the distinction by making everyproperty a “soft” point expression, which may, in the simplest case,equal a single hard point.) Points are normally stored asvalue-timestamp pairs: a value, and the timestamp when it was recordedor calculated. A series of value-timestamp pairs gives the history ofthat point. It's also common to store additional metadata with tags,such as the path to a field device or PLC register, design timecomments, and alarm information.

A Human-Machine Interface or HMI is the apparatus which presents processdata to a human operator, and through which the human operator controlsthe process. An HMI is usually linked to the SCADA system's databasesand software programs, to provide trending, diagnostic data, andmanagement information such as scheduled maintenance procedures,logistic information, detailed schematics for a particular sensor ormachine, and expert-system troubleshooting guides. SCADA solutions oftenhave Distributed Control System (DCS) components. Use of “smart” RTUs orPLCs, which are capable of autonomously executing simple logic processeswithout involving the master computer, is increasing. A functional blockprogramming language, IEC 61131-3, is frequently used to create programswhich run on these RTUs and PLCs. Unlike a procedural language such asthe C programming language or FORTRAN, IEC 61131-3 has minimal trainingrequirements by virtue of resembling historic physical control arrays.This allows SCADA system engineers to perform both the design andimplementation of a program to be executed on an RTU or PLC.

For example, in one embodiment of the present invention, the predictiveindicator system provides a threat level index for a weather event to amunicipal electric utility company. Infrastructure data including thelocation of above-ground power lines and trees can be infrastructure andnatural environmental inputs into the system. The system can thenanalyze the likelihood that winds over a certain speed will occur and/orthat icing will occur in an area where both above-ground power lines andtrees are present. Where it is determined that all three of thesefactors are likely to overlap, a high threat level is assigned. Thus, anEmergency Management Center of an electric utility company can benotified and therefore, be able to decide where and when to martialmanpower and how much manpower to allocate before the crisis or outagearises.

As another example, in another embodiment, the predictive indicatorsystem provides a threat level index for a weather event to a sewagedepartment. In such a situation, the static infrastructure data mayinclude data regarding the location and capacity of waterways anddrains, and possibly the topology of the area. The system would be ableto predict the location of heaviest rainfall in a city and the directionthe rainwater would travel once on the ground, and determine whether thesewage department would then need to reroute certain waterways toattempt to load-level the rainfall volume across a larger portion of thecity. Thus, Emergency Response Management would be able to decide inadvance where and when to reroute such waterways.

As a third example, in another embodiment, the predictive indicatorsystem provides a threat level index for an environmental hazard, suchas a chemical spill, or for a major catastrophe, such as a nuclearaccident. In such a situation in which a harmful agent is released intothe air, the location and amount of the release would be input into thesystem, along with infrastructure data such as the location of schoolsand heavily populated areas. When the location and volume of the releaseis correlated with the wind direction and speed (and possibly with rainmovement and humidity index in the case of a water soluble release), athreat level can be assigned to those areas with the highest populationsand/or highest chance of encountering the release cloud. Further,knowing precisely what the speed of the wind will be with fineresolution allows for the calculation of the probable amount of time itwill take for the release cloud to encounter infrastructure. Thus, aFEMA and/or a business's Emergency Response team would be able to decidewhere and whether to evacuate, and where to send the evacuees.

As a fourth example, in another embodiment, the predictive indicatorsystem provides a threat level index for a fire created by a majorcatastrophe, such as a fire caused in the wake of an earthquake or othernatural disaster, or possibly a wildfire. In such a situation in whichfire is spreading, the location and size of the fire would be input intothe system, along with infrastructure data such as the location of otherflammable material, chemical holding facilities, heavily populatedareas, etc. When the location and size of the fire is correlated withthe wind direction and speed (and possibly with humidity and rainfall),a threat level can be assigned to those areas with the highestpopulations and/or highest chance of the fire spreading. An examinationof previous localized rainfall and topography can also be utilized.Further, knowing precisely what the speed of the wind will be with fineresolution allows for the calculation of the probable amount of time itwill take for the fire to encounter infrastructure. Additionally,wildfires caused by lightning could be predicted by correlating very dryareas with the specific areas predicted to encounter heavy lightning.Thus, a FEMA will be able to decide where and whether to evacuate, andto where to send the evacuees.

The EAS process includes a network of weather stations deployed in keylocations throughout the region that measure various conditions,including temperature, humidity, atmospheric pressure, wind speed andrainfall rates. These stations can be solar-powered and can continuouslyfeed information to a central site where the data is quality-controlled.The quality-controlled data is then placed in a database where it can beaccessed by other tools with the EAS process to create graphics of theexisting conditions and for use in numerical weather prediction models.The Mesonet data and the high spatial and temporal resolutionmeteorological forecast is not the only method utilized to understandand create an assessment of the current and predicted weather, and toasses the risk that the weather posses to the utility industry and tothe government agencies responsible for emergency management.

To properly assess the current and predicted weather and to asses therisk, the temporal and spatial resolution is maximized and the currentand predicted meteorological fields are used to derive and displayparameters such as divergence, vorticity, moisture advection, velocityshear and deformation, and gradient wind strength. Using the EAS processdeveloped tools a clear and decisive assessment of the current andpredicted weather and the risk this weather poses can be clearlyunderstood and delineated. The mesonet stations are also strategicallyplaced based on regional characteristics, known geophysical trends orconditions, maximizing spatial and temporal resolution and the relevantapplication. The EAS process develops a characteristic scheme thatutilizes regional information including topography, climatology andinfrastructure, high spatial and temporal resolution information andapplication. The meteorological fields described and how they would becombined should be apparent to one, skilled in the art area as theyreview the entirety of this specification.

Using the analysis created by the EAS process, an indication of thoseareas most at risk for losing power or other threat is produced. Thesystem rates the probability of power loss or other threat in individuallocal areas on a scale from 1 to 100. A state of the art weatherprediction model (WRF) is tuned to fit the local conditions by choosing,from the many possible choices of parameterization schemes, thoseparameterizations schemes that, when combined, provide the best possibleforecast. The present invention determines which combinations ofparameterization schemes that produce the highest spatial and temporalcorrelation factors for the application, spatial and temporal resolutionand region between the forecasted weather conditions and observedweather conditions. All possible parameterization scheme optionsreflected in Appendix 1 and a representative determined scheme isreflected in Appendix 2.

The larger scale numerical weather forecasts from the National WeatherService are combined with the quality controlled high spatial andtemporal resolution data from the Mesonet using the model initializationtools produces a significantly improved WRF model initialization thatcreates a forecast with the highest spatial and temporal correlationfactors between observed and forecasted weather data. The high spatialand temporal resolution weather forecast is combined with the locationof overhead power lines, and tree density and other natural obstructionsor terrain and type to create an index that provides the power industryor other relevant user with an indication of the precise location ofthreatened areas, when these areas will be affected and for how long.This high spatial and temporal index allows the utility industry topreposition equipment and crews to minimize the disruption to theinfrastructure. The advanced warning also allows the utility industryand other entities requiring emergency response management to maximizethe allocation of personal. When applying the EAS system to otherapplications other infrastructure, topography and natural environmentfeatures may be used.

The EAS Process creates accurate, location-specific weather hours aheadof storms, thus users will be better prepared for severe weather. Thissystem is tied to information on trees (and other natural environmentalconditions such as for example—terrain and elevation) and power lines(and other infrastructure, such as for example roads or man-madeobstructions and structures that can impact the readings of sensorstations or impact of local weather), it also enables users to morereadily—and quickly—identify areas that have been hardest hit by outagesor damage. Data such as population density, housing density, criticalfacilities, pedestrian and vehicle traffic can be utilized. This is amore efficient use of financial and human resources and willsignificantly improve the company's response and restoration time forcustomers, improving system reliability.

The system's usefulness extends beyond pinpointed weather forecastingand the utility industry, the utility industry is utilized throughoutthe specification as an illustrative example, but in no way limits thescope of the present invention. For example, because the systemconstantly monitors wind conditions, the system would have the abilityto predict the path of a cloud of hazardous material in the event of anindustrial accident or bioterrorism attack.

BRIEF DESCRIPTION OF THE DRAWING

For a better understanding of the present invention, reference may bemade to the accompanying drawings in which:

FIG. 1 is an illustration of EAS network environment;

FIG. 2 is a functional illustration of EAS;

FIG. 3. is a functional illustration of a sensor suite;

FIG. 4 is an over functional flow of the EAS process;

FIG. 5 is a functional flow of the National Weather Service Data IngestProcess;

FIG. 6 is a functional flow of the Mesonet Data Ingest Process;

FIG. 7 is a functional flow of the Mesonet Quality Control Process

FIG. 8 is a functional flow of the Mesonet Graphics pre-processor;

FIG. 9 a is a functional flow of the Forecast Model;

FIG. 9 b is an illustration of a three-dimensional rendering of forecastdata;

FIG. 9 c is an illustration of forecast data;

FIG. 9 d is an illustration of threat level data; and

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription presented herein are not intended to limit the invention tothe particular embodiment disclosed, but on the contrary, the intentionis to cover all modifications, equivalents, and alternatives fallingwithin the spirit and scope of the present invention as defined by theappended claims.

DETAILED DESCRIPTION OF THE INVENTION

According to the embodiment(s) of the present invention, various viewsare illustrated in FIGS. 1-9 and like reference numerals are being usedconsistently throughout to refer to like and corresponding parts of theinvention for all of the various views and figures of the drawing.

Data from all over the entire globe is typically utilized in order tomake a forecast. Meteorological forecasting is performed in a globalfashion. Global forecast data is available to forecasters around theworld. There is a very large global model referred to as GFS, whichstands for the Global Forecast System. Unfortunately, it is difficult tomake that model work on a localized level in order to predict weatherevents accurately because the resolution of the model is verycoarse—lacks sufficient resolution. Further, such a forecast systemlacks the ability to factor in local parameters that may affect theaccuracy of a prediction and further predict the impact that the weatherevent may cause. Depending on the season of the year the resolution canvary and can be somewhere between 40 and 50 kilometer resolution. Thisresolution is inadequate for use in a highly localized forecastingmodel. The important features that are of interest are missed. That is acommon problem with all groups that make use of these weather forecasts.The resolution from this global forecast model is too coarse.

In order to solve the problem, individual groups have attempted methodsof nesting one model with another. A larger global forecast system modelis generated and then nested with a model generated across thecontinental United States, and further the National Weather Service runsyet another model that's called the North American Meso Model, orsometimes referred to as NAMM. That's a finer resolution model and it'supdated more often. However this level of nesting is still not goodenough for the kinds of forecasting that is addressed by the presentinvention because the resolution is still too coarse and can not bemanipulated or extrapolated as needed and still does not factor in localparameters that may affect the accuracy of a prediction and furtherpredict the impact that the weather event may cause. However, as theinvention is further described herein, it will become apparent thatincreased resolution by itself particularly at this level also is notsufficient. However, the resolution resulting from nesting to this levelis fine enough that it can be utilized by the present invention if theproper initialization and pre-processing is performed by the presentinvention and combined with the local Mesonet data of the presentinvention to create the present invention's WRF model, which stands forWeather Research Forecasting model. The finer resolution of the WRFmodel can better controlled, manipulated, parameterized and modified. Byutilizing the present invention, the fineness of the resolution both inspace and time can be selected by the user.

Although more information is needed than either of these two modelsnested can provide me, the present invention can utilize the informationto create what can be referred to as boundary conditions. The boundaryconditions can be modeled as a cube or a matrix of cubes, which defineboundaries over which the present invention can be utilized to performforecasting. The cube or boundary can be considered a geographic regionand the nested data as described can be utilized to determine what theconditions are along the edges of this model.

Conditions defined along the edges create a boundary for my model. Theboundary includes a lower boundary or something at the ground as well assomething along each of the side edges. Some of the information can beproperly extracted by the EAS system from the medium resolution model.Additional information can be captured from the EAS systems localmesonet that has been strategically installed and implemented. The EASsystem can also capture geographic information from a geographic database. And this geographic data base can include topography, soil type,soil moisture, soil temperature and other relevant climatologicalgeographic information from a climatological model. Further, land useinformation can be extracted such as for example—it's a farm, it's ahighway, it's a baseball stadium. Topography, soil type, moisture,temperature, land use information and other like information can beextracted in order to describe the lower boundary of the model, or ifyou will, domain. The cube or other practical geometry that is utilizedby the EAS system can be referred to as the model domain. In the case ofa cube, the model has six sides to the domain. Four edge domains can beextracted from the coarser or medium scale forecast. The bottom can beextracted from the above mentioned geographic database information,which was developed as part of the WRF model. It has a resolution ofabout approximately 30 arc-seconds which is what. This allows for veryfine transitions in the environment to be discerned. The topography datais available from databases such as the database generated by theNational Defense Mapping Agency.

The EAS system can be utilized to determine the pieces of thatgeographic database that is need for a particular model domain. TheGeogrid routine of the EAS computing system can be utilized to take thedefinition of the domain or model domain. The Geogrid can access thedatabase and extracts out of it the information that is needed todescribe the bottom portion of the domain referred to as the lowerboundary condition. The domain can be changed and the Geogrid can adaptthe domain accordingly. This domain can be changed and defined based onneed. It is this ability to adapt the domain and the prediction needthat drives the location of the stations. The need can be time andspatial dependent, in other words—on one hand how far in advance is theforecast needed or the forecast time window, and this can be generalreferred to as the temporal resolution. There's a second portion of theresolution that has to do with spacing or spatial resolution. This againis driven by need. For example the forecasting need for a largemunicipal electrical utility that is concerned about the impactinclement weather may have on their infrastructure, the spatialresolution needed can be about approximately 9 kilometers. The temporalresolution in this example can be about approximately 15 to 20 minutes.So as the need changes the Geogrid can be utilized to modify the domain.So the parameters that are utilized for the Geogrid come from thedefinition of the need. The Geogrid can be a computer implement functionor routine.

An additional piece in the EAS system is a program or function referredto a Ungrib. Unigrib takes the large coarse domain and decodes it.National Weather Service, for example, broadcasts this weather datainformation for access. The data is usually shipped as a large data filethat is compressed and can be generally referred to as a GRIB file,which stands for a gridded binary file. The UnGRIB function decompressesthe file. Then, Metgrid takes the lower boundary condition, and it takesthe lateral boundary conditions generated by UnGRIB and imports theinformation into the defined domain. Now all of the boundary conditionsthat surround the defined domain have been modeled. The EAS system nowhas an accurate description of the initial conditions and the boundaryconditions for the model domain.

The MetGRID, GeoGRID, and UnGRIB functions can be generally referred toas the Mesonet pre-processor function. Another component of the Mesonetpre-processor function is the quality control function that operates onthe Mesonet data. The Metgrid processer takes all of that information,and transforms the data into the conditions that are necessary for theEAS system to run my model. An advantage that is provided with the EASsystem is the information that goes into Metgrid function is processedby the quality control function which performs a quality controlfunction on the mesonet data. The EAS system, also generally referred toas the Quantum Weather system, utilizes data from the EAS local mesonetand refines the data and provides a quality control function. The EASsystem incorporates all the extra local mesonet provided informationthat is at a much finer scale. A function of the EAS system is importmesonet data into Metgrid. However, the pre-processing including qualitycontrol provides for greater accuracy. The pre-processor function can beimplemented with a computing system comprising the computing elementsand components and functional computing modules illustrated in FIGS. 4,8 and 9 a.

For example, a lot of information that comes out of this mesonet isaffected by things that aren't related to the weather. A classic exampleis when a municipal utility company has infrastructure that is downwindfrom a very large sign or other like obstruction on the side of theinterstate such that the sign or other obstruction acts somewhat as awind-shield. Even if a wind sensor is located in the area, the wind seenat the infrastructure will differ from that seen in the immediatelysurrounding area. The same thing can occur with building or naturalobstructions. The EAS system can also perform other quality controlfunctions like making adjustments for sensor station placement or makingadjustments when there is an atmospheric tide in the atmosphere thatactually causes the pressure—even when there is no weather—to vary in acyclical manner, or making adjustment when the wind direction, windspeed, the temperature and pressure are correct, but the relativehumidity always reports zero. In addition to the quality controlfunction and the Mesonet pre-processor function also performs anassimilation or ingest of data function, where Metgrid incorporates datainto the model.

The domain as described above can be broken up into a series of littlecubes. Viewing the domain from the top down picture shows a grid ofsquares. However, sampling of the atmosphere, is not only just on theground, but is also vertical. The method that EAS uses to solve theequations forward in time requires that we have the information (initialconditions) at regular uniform intervals in space, and these cubesshould be uniform in size. They all should be the same size andinformation is needed at the cube intersections. However, typicallysensors, thus data, is not available at those grid intersections. Forexample, typically one of the biggest users of weather information inthe ‘20’s, ‘30’s, ‘40’s and ‘50’s was airports. Therefore, a majority ofthe National Weather Service observations are taken at airports. Sotypically sensor station have been somewhat clustered around majorcities, thus not importable in a uniform grid. Importing the informationor data into a nice rectangular uniform grid can be referred to as anobjective analysis.

Therefore, not only are the stations from the National Weather Serviceirregularly spaced, they are also not strategically space for the localmesonet. Therefore, the ability to transform the information in anobjective fashion onto the individual grids is problematic. With thepresent invention, there is an objective analysis method that is used toperform this function. There is a method that is utilized by the EASsystem that can generally be referred to as the ObsGRID function thattransforms all of the information from an irregularly spaced network ofstations and puts imports the information on uniform cubical grids in afashion that is sufficiently accurate for the necessary predictions. ANyquist sampling technique can be utilized, which can be used along witha Barnes and/or Cressman sampling technique. This transformation isnecessary for the ingest process.

ObsGRID transforms the data in a manner that can be ingested by MetGRID.Obsgrid generates something that Metgrid can read and it takes all ofthe pieces together, this material from the objective analysis, theNational Weather Service, the lower boundary conditions, describing thetopography, the land use, the soil moisture, soil temperature, and allother information, and combines it together to produce for the domain, amodel domain, having lateral and lower and initial boundary conditions.The model boundary conditions describe the initial state of theatmosphere. In order for this model to work for prediction purposes, theEAS system accurately describes the state of the atmosphere when themodel starts, which can be referred to as initial conditions. Further,the EAS system updates data at such a frequency that ability toinitialize the model at an arbitrary instance in time is highlyselectable. Typically previous systems could only initialize a forecastmodel at six (6) hour intervals and the upper atmospheric conditions maybe data from a previous 12 hour interval. As indicated above sampling ofthe atmosphere with the EAS system occurs not only just on the ground,but it is also performed in the vertical as well. Therefore, EAS hasstrategically placed stations that samples in the vertical, whichdiffers from the National Weather Service. So the data is in a fullthree dimensions. In addition to the three dimensions the EAS system, asdiscussed above can initialize the model at selected arbitrary times,which adds what can be referred to as a fourth dimension—three spatialdimensions and one in time, as well. So EAS is working in fourdimensions and not just three because of the near real time data.

When the EAS is developing a forecasting model, the parameterizationscheme can be tuned to address the specific forecasting need. Forexample, if high winds are of concern because of a potential adverseimpact on an asset of concern then the EAS system must be able toforecast with a high level of accuracy the location of the high winds.To do an accurate forecast the EAS system will need to make sure to tunethe model to produce the highest quality convection, i.e, thethunderstorms and clouds, and associated with that highest qualityconvection cloud forecast, a high quality wind forecast. Differentparameterization schemes may be selected in order to optimize theforecast. Different parameterization scheme may be different dependingon the different forecast need. Further, the EAS local Mesonet data canprovide near real-time conditions such that the selection of theparameterization scheme can be based on that real-time information.There are generally four (4) categories of parameterizationschemes—Surface Layer Parameterization having three (3) schemes,Boundary Layer Parameterization having four (4) schemes, Micro-physicshaving eight (8) schemes, and Cumulus having six (6). For example if amunicipal electrical utility has a need where they are concerned aboutdamage to assets caused by high winds, then EAS can select aparameterization scheme optimized for convection and the high windsassociated with it. That now chooses how EAS will parameterize certainfeatures within the model.

An example of how a parameterization scheme works is as follows, if EASdefines on of the cubes in the model as 1 kilometer on the side and 500meters tall. For some clouds—or for some types of clouds—the systemwould completely fill this cube with the cloud, and may even extend thecloud on either side of the cube in the model. In which case, the systemcan describe the processes within the cloud very easily with this 1kilometer cube. However, what happens when there is just the beginningsof a thunderstorm and it just begins to start up, and it just fills uponly a portion of that 1 kilometer cube. The system couldn't describeaccurately that cloud entirely. However the system can explain ordescribe its effects on what's going to happen within this 1 kilometercube. There is a method that can be used such that even though it can'tsee the cloud itself, it can see the effects of the cloud on thatparticular cube. That's known as a parameterization scheme.

As mentioned above, there are various types of parameterization schemes,such as for example, there's a boundary layer parameterization scheme.Very close to the ground, for example, there is a layer that is aboutapproximately 500 meters deep or typically 500 meters deep. There can bea lot of turbulence within that 500 meter layer. The system can'tdescribe all of that turbulence within that little 500 meter depth, butit can tell you what effect that turbulence has on this one whole blockof the atmosphere—this one little 1 kilometer cube. So there's aboundary layer parameterization. There is also a cumulus cloudparameterization. Inside of clouds, there's this whole process where iceis converted into—well, I raise air up in the atmosphere, it cools off,it forms water, it forms ice droplets, it rains. That process is far toosmall scale to describe on this 1 kilometer square, however, it candescribe the effects of that process on what's happening inside thatcube. Those are known as parameterization schemes.

The model can provide choices. If running the model at a very fineresolution, 200 meters, then do not need a cumulus parameterizationscheme. The system can accurately describe the cumulus clouds at 250meters. The model allows for choices to be made. For the boundary layer,if data is available, the Metgrid/Obsgrid process will allow the first500 meters to be described if the observations are available. The modeland the Obsgrid is generalized and flexible enough so that it can getthe data, it can be process and the parameterization scheme can actuallybe removed and the boundary layer can be describe exactly. The actualreal-time data from the EAS local Mesonet can provide information.Therefore, the system is flexible enough that it can choose between justdescribing the effects or actually reproducing whatever is happeningthere in the atmosphere completely.

A database of the most effective parameterization schemes for a givenneed can also be assembled and accessed by running the model with andwithout various surface layer parameterizations with and without cumulusparameterization and by taking a look and verifying the quality of theforecast for each one of these possible choices of parameterizationschemes. Within, for example, the cumulus parameterization, there isactually a selection—there's three or four options that can bechosen—there's three or four different cumulus parameterization schemesand you can pick one. Or you could pick the other. Or you could picknone and actually reproduce the atmosphere entirely. The net result is adecision about—for a specific problem, what particular choice providesthe best quality forecast, and the outcome would likely be different fordifferent applications. The refinement of the optimization of theparameterization scheme is something that is an on going process. Thereis an automated tool that generates a skill score that rates theeffectiveness of a parameterization scheme for a given need. The EASsystem can review the skill score daily and so long as nothing happensor that skill score doesn't change dramatically, the system will have noneed to make corrections. Other times, it is a flaw within the model orthe parameterization scheme itself, which can be corrected.

There are different forecast and prediction needs depending on the assetof concern. In the case of a municipal electrical utility the assets ofconcern may be power lines and the like and the threat of concern may beicing. Typically an 8 hour window of warning is need to appropriatelyplan for such a weather event. However, in the case of a sporting eventyou may need less than an hour window of notice. For example, in thecase of a baseball event you may need only about 5 minutes or 10 minuteswarning to cover the field. In other words—what is happening now in thenearby surrounding area. The local mesonet of the EAS system will solvethis problem and actually will watch the progression of the storm moveacross the local mesonet and when it gets to about 10 minutes away, forexample, can provide adequate warning.

So part of the solution provided by EAS to the question of how big thedomain is and how to place stations is very dependent on the particularproblem that is trying to be solved. It is a time problem in the senseof how much is needed. It is also a spatial problem because EAS mustdetermine how much spatial coverage is needed or how far to go back toget enough upwind or upstream data. So, the placement of the sensorstations for EAS is dependent in space and time on how much lead time isneeded. There is a spatial and temporal resolution. The location ofassets of concern is also a driver for placement of the sensor stations.Further, unique natural or land-use or infrastructure conditions can bea driver as well as known prediction problems. EAS has an optimizationscheme to address the various needs.

There also may be areas of interest were EAS has determined an effectiveparameterization scheme in a particular region is not feasible thusactual data is needed thus driving the placement of sensor stations inthe local mesonet. EAS is designed with locations to fill in gaps whereinformation is needed to have an answer to or description of conditionsso that EAS can report what is actually happening on a regular basisbased on what is seen across the network in terms of how much longer,for example the icing event, is going to continue and how heavy theicing was going to be and how fast is it accumulating. The EAS systemalso allows for user input to make adjustments just based on userknowledge of current conditions or historical knowledge. Thus a user canalter the boundary layer parameterization scheme. There's a differentchoice for that particular combination. And so there's a list of these,all of these combinations that we, you know, have, there's this greatbig table that says if this is happening, then go pick this particularset of parameterization schemes.

EAS can also provide a threat index for an event that is designed toidentify problem areas of interest that merits action or a closer watch.The threat index level can be determined by considering a combination ofthree factors including 1.) the type of asset; 2.) weather conditionsthat can potentially place the asset at risk; and 3.) special conditionsin the area that can potentially amplify the threat. In the case of anelectrical utility, the asset could be power lines or high tensiontowers. The weather conditions could be icing or high winds. The specialconditions could be trees hanging over the power lines. The threat indexcould be a level rating of 0 to 100 where 100 is the highest threatlevel. EAS determines at what geographic locations and what predictedtimes will all three of those conditions exist and attach to that anumber.

Therefore, for example, if you have winds of about approximately 70miles an hour predicted 3 hours from now in a particular location andyou there are overhead structures in the area and they have treeslocated there that are full of leaves and the soil moisture ishigh,—this will likely result in a very high threat index. However, ifyou have a location where there's hardly any trees and there's nooverhead lines because it's all new construction, then the threat indexwould be substantially lower. A map grid of an area can be generated andthe threat index data can be overlayed over the map. A graphicalrepresentation of high winds or icing for example can be provided aswell as color high-lighting various regions having various threatindexes. The map will evolve and transition over time as the weatherthreat passes over. The system can also take into considerationhistorical climatology such as for example historical freeze lines toadjust prediction and this climatology information can also be used forEAS sensor station placement.

Utilizing the SCADA communication network available over power lines hasfacilitated the ability to place EAS sensors in areas that would havetypically required satellite or short wave communication capabilitybecause in some areas there are no communication means. There aren'tvery many places in the US where there isn't electric power. This SCADAinterface allows the EAS sensor stations to make an Internet connectionacross a power line. And so that means now wherever there's a power lineor some sort of electric power socket or something else like that, EAScan gain access to the SCADA communications network that runs on thepower lines. SCADA is a standard protocol system control access and dataanalysis network. SCADA probes the entire power line infrastructureevery few milliseconds. To access SCADA, an electronic conversion deviceis utilized to translate the SCADA protocol to that of a standardethernet. So that means that an EAS sensor station can be placedanywhere there is an electric power line. The strategically placed EASsensor station can monitor and communicate time, temperature, relativehumidity, pressure, wind speed, wind direction, precipitation and thebattery power or the charge rate due to the level of solar energypresent, which tells how much sunshine. Use of an electric utility SCADAnetwork to communicate information from the remote weather stationsfacilitates the ability to establish a local Mesonet network.

One embodiment of the present invention comprising a localized near termweather prediction function, a current static state environment input, acurrent static state infrastructure input, and a threat analysisfunction teaches a novel apparatus and method for a threat level indexto predictor which indicates the localities which are most likely to bethreatened by the exacerbation of an event by the weather, therebyequipping Emergency Response Management to then use the threat levelindex to determine where and when to martial personnel.

The EAS Process can use a solar powered, wireless Ethernet connectedmeteorological sensor suite. The solar charged battery supply isguaranteed to provide continues operation below 60 degrees latitude andwill operate for at least 60 days without sun. Since there is noconnection between the sensors and the power line, damage fromthunderstorms is virtually eliminated and the sensors are immune topower line voltage surges. There can be wireless connectivity betweenthe sensor station and the SCADA substation. The SCADA substations arelocated in areas where there are power lines. Thus the substations canbe communicably connected via wireless transceivers to the EAS stations.The initial sensor calibrations for wind, temperature, relative humidityand barometric pressure are traceable to the National Institute ofStandards and Technology. The passive solar shield for the temperatureand humidity sensors are modeled after the ones designed by the NationalWeather Service. The meteorological sensor suite is capable of samplingthe data every 2 seconds and transmitting data every 2 seconds. Thus thesensor suite is able to collect near real-time data. It is important tonote that any wind gusts are not missed as the peaks are captured. Inthe current configuration, data is averaged over a one minute periodbefore being transmitted to the central site. The averaging interval isremotely adjustable to handle different meteorological events. Thesensor suite includes:

Anemometer

-   -   Range: 0-67 meters per second    -   Resolution: 1.0 unit.        -   1 meter per second        -   1 degree    -   Accuracy: ±2% of full scale.

Temperature

-   -   Range: −66° to +166° F.; −54° to +74° C.    -   Accuracy: ±1° F.; ±0.5° C.

Relative Humidity

-   -   Range: 0 to 100% RTY.H.    -   Accuracy: ±2% at 25° C. Temperature compensated from −40° to 85°        C.

Barometer

-   -   Range: 551 to 1084 Millibars (hPa), absolute reading. Digital        offset for site altitude.    -   Accuracy: ±1.69 Millibars (hPa) at 25° C. Temperature        compensated from −40° to 85° C.

Rainfall

-   -   Range: Unlimited Tipping bucket with 8-inch diameter collector.    -   Resolution: 0.01″; 0.25 mm.    -   Accuracy: ±2% at 1 inch per hour.

Transceivers

-   -   Range: Up to one-mile line of sight. Walls or other RF absorbing        structures may reduce range.    -   Frequency: 2.4 GHz spread spectrum 802.15.4. F.C.C. Approved.

Given that SCADA is a standard protocol system control access and dataanalysis network and that SCADA probes the entire power lineinfrastructure every few milliseconds, the ability for EAS stations tocommunicate across the SCADA network alleviates the need to invest in ahuge infrastructure. The wireless transceivers at the substations can beequipped with an electronic module that take data transmissions from thelocal Mesonet stations and convert the transmission to a SCADA protocolformat that can be transmitted across SCADA to a central location wherethe transmission can be converted to transmit the data via the interneto the EAS computing systems. The universal protocol of SCADA forelectrical utilities across the country makes the present inventioneasily transportable from one region to another.

In order to insure that only correct data from the Mesonet areincorporated into the EAS process the data from the Mesonet stations iscarefully examined. The data from each station is checked to insure thatthe values are within realistic ranges (level 1 check). The data is thencompared against values for the same station for previous times (level 2check) to insure that a single miss-transmission of data has occurred.The data is then compared with data from nearby stations to insure thatseveral miss-transmissions or contaminated data have not occurred. Inaddition to the data from Mesonet, the EAS process also receives thelarger scale model initialization data. The EAS process then performsthe analysis of the existing weather conditions, creates the highspatial and temporal resolution forecasts, and creates the analysis andforecast graphics and web pages. A specialized computing facility isneeded, and in this instance a High Performance Computing (HPC) facilitycan be used.

Traditionally, computer software has been written for serialcomputation. To solve a problem, an algorithm is constructed andimplemented as a serial stream of instructions. These instructions areexecuted on a central processing unit on one computer. Only oneinstruction may execute at a time—after that instruction is finished,the next is executed. Parallel computing, on the other hand, usesmultiple processing elements simultaneously to solve a problem. This isaccomplished by breaking the problem into independent parts so that eachprocessing element can execute its part of the algorithm simultaneouslywith the others. The processing elements can be diverse and includeresources such as a single computer with multiple processors, severalnetworked computers, specialized hardware, or any combination of theabove.

The single-instruction-single-data (SISD) classification is equivalentto an entirely sequential program. The single-instruction-multiple-data(SIMD) classification is analogous to doing the same operationrepeatedly over a large data set. This is commonly done in signalprocessing applications. Multiple-instruction-single-data (MISD) is ararely used classification. Multiple-instruction-multiple-data (MIMD)programs are by far the most common type of parallel programs. Parallelcomputers can be roughly classified according to the level at which thehardware supports parallelism. This classification is broadly analogousto the distance between basic computing nodes. These are not mutuallyexclusive; for example, clusters of symmetric multiprocessors arerelatively common. A cluster is a group of loosely coupled computersthat work together closely, so that in some respects they can beregarded as a single computer. Clusters are composed of multiplestand-alone machines connected by a network. While machines in a clusterdo not have to be symmetric, load balancing is more difficult if theyare not. The most common type of cluster is the Beowulf cluster, whichis a cluster implemented on multiple identical commercial off-the-shelfcomputers connected with a TCP/IP Ethernet local area network.

The HPC facility makes use of Message Passing Interface (MPI) standardto decompose a program that is to be run on a cluster into segments thatcan be safely run on individual nodes of the cluster. The primary ormaster node that initiates the process passes a segment to theindividual nodes in the cluster via the MPI software. Once each node hascompleted its segment, it passes the information back to master nodeagain via the MPI software. The EAS process currently uses COTScomputers from Sun Microsystems running the Solaris operating system.The use of clustered COTS computers allows the creation of a HPCfacility at minimal cost.

Connecting together the individual weather stations would traditionallyrequire a dedicated Ethernet or a radio to transmit the data from theweather station to the central facility. The dedicated Ethernetconnection has proven to be too expensive to use in an ongoing basis andis only in use for special short-term projects. Although the radio basedlinks are possible their limited bandwidth prevent rapid transmission ofdata from the remote site to the central facility. Working incooperation with the electric power industry, the data from the remotestations are connected to the power industry's Supervisory Control AndData Acquisition (SCADA) network. The SCADA network makes use of thepower lines to monitor and control the electric power grid, monitor andread electric power meters and notify the power company of anomalousconditions. This high bandwidth connection allows the weather data to bepiggy-backed onto the existing data stream. This allows the weatherstations to be monitored continuously. Since the electric power industryhas facilities located throughout any region the networked weatherstation can be placed anywhere the electric power industry or any othergroup making use of the SCADA network is located, including watertreatment and distribution, wastewater collection and treatment oil andgas pipelines, and large communication systems.

The details of the invention and various embodiments can be betterunderstood by referring to the figures of the drawing. Referring toFIGS. 1 and 2, an illustration of one embodiment of an EAS environmentis provided. As previously discussed the sensor arrays can be remotelylocated strategically based on regional characteristics, maximizingspatial and temporal resolution and the specific application, forexample an electrical utility. The sensor array includes an anemometer,a temperature sensor, a humidity sensor, barometer, a rainfall detectorand a wireless transceiver. The sensor array must be place atop anelectrical pole, tower or other high structure that allows for mountingof the sensor array such that the sensing is unobstructed. The sensorarray can be solar powered, battery powered or powered by a proximateelectrical line. A battery power source can be rechargeable that isrecharged by a hardwired electrical source or by solar power.

The transceiver can transmit the sensed data wirelessly to a substationthat can buffer the data and transmit the data via an Ethernet interfaceto a SCADA network. For example in the utility industry, SCADA networksare often already in place to monitor the operation of theinfrastructure, for example the operation of a substation or atransformer. An existing SCADA network will have a data protocol whetherthe protocol is proprietary or a standard protocol. Therefore aconverter for converting the sensor data to a protocol or format of aSCADA network may be necessary. A converter or concentrator can beinstalled at the input of the SCADA system for converting the sensordata. Once the sensor data is converted, it can be sent to a SCADAserver which can process the data for transmission to a central locationwhere the data can be evaluated. The information can be transmitted bythe SCADA server over a wide area network, for example the Internet, toa EAS system. The EAS system can receive the information sent over awide area network through a proxy server, which can transmit theinformation to a EAS master node server. The EAS master node server candecompose and parse the data for further processing by the multi-coreclient node servers.

Placement of a sensor station for the local mesonet can be determined byconsidering three primary parameters and they are—1.) the type ofweather threat/event of concern; 2.) the type and location of the assetthat is potentially threatened; 3.) extraneous unique local conditionsthat may render an otherwise predictable environment unpredictable ormagnify the threat level of the weather threat/event even when theweather threat/event would have normally been a non-event. Item 1.),though considered, can be rendered of little or no effect if each sensorstation placed has the entire suite of sensor types and the sensorstation is optimally placed such that each sensor type will get a goodreading. The stations also can have the ability to sample in thevertical. However, placement of a station for optimal reading of allsensor types will be overridden if the asset of concern is mostthreatened by a particular type of weather threat/event such thatplacement is skewed toward a placement that provides best sensingcapability for that particular type of weather threat/event that theasset is most endangered by. For example, a power line may be mostthreatened by icing or high winds. Therefore, the most heavily weightedparameter can likely be the type and location of the asset that ispotentially threatened. Placement of sensor stations are made to providethe best coverage for the assets of concern and placement of stationsare skewed toward a placement that allows for best sensing of a weatherthreat/event of most concern. Item three can be natural or man madeobstructions or objects or climatologically induced conditions thathinder normal predictive capabilities or that can increase the threatlevel above what it would otherwise normally be. For example, a buildingmay hinder an accurate reading of wind conditions. Traditional placementof sensor stations have typically resulted in placement of sensorstation in and around major airports because of there typical proximateto large metropolitan areas. If it is decided to install an EAS systemin an region, then sensor stations can be installed strategically atvarious locations as dictated by the above parameters in order to form alocal mesonet network, which provides coverage for the assets of concernin the region. The local mesonet can be implemented and layered incombination with other available data as discussed above.

Referring to FIG. 3 an illustration of a representative sensor suite isprovided. The sensor suite can include an Anemometer sensor having arange of about approximately 0-67 meters per second and can have aresolution of 1.0 unit, 1 meter per second, 1 degree with an accuracy of±2% of full scale. The sensor suite can have a temperature sensor havinga range of about approximately −66° to +166° F. (−54° to +74° C.) withan accuracy of about approximately ±1° F. (±0.5° C.). The suite caninclude a sensor to detect relative humidity having a range of aboutapproximately 0 to 100% RH with an accuracy of about approximately ±2%at 25° C. and with a temperature compensated from about approximately−40° to 85° C. The suite can include a barometer having a range of aboutapproximately 551 to 1084 Millibars (hPa), with an absolute reading, adigital offset for site altitude and an accuracy: ±1.69 Millibars (hPa)at 25° C. and temperature compensated from −40° to 85° C. The suite caninclude a rainfall detector having an unlimited tipping bucket with8-inch diameter collector with a resolution of about approximately 0.01″(0.25 mm), and an accuracy of about approximately ±2% at 1 inch perhour. The suite can include wireless transceivers having a range of upto one-mile line of sight with a frequency of 2.4 GHz spread spectrum802.15.4. F.C.C.

Referring to FIG. 4, an illustration of an EAS operational flow isprovided. The operational flow has three primary functional flows. TheEAS process begins with ingest of data from two different data streams.The first data stream is the observations and global model forecastdata. The second data stream is the observations from the EAS processMesonet. The data stream from the National Oceanic and AtmosphericAdministration consists of observations of the three-dimensionalstructure of the atmosphere and large-scale forecasts from numericalweather prediction model run by the National Weather Service. However,not only are the stations from the National Weather Service irregularlyspaced, they are also not strategically space for the local mesonet.Therefore, EAS transforms the information in an objective fashion ontothe individual grids. There is an objective analysis method that is usedto perform this function. There is a method that is utilized by the EASsystem that can generally be referred to as the ObsGRID function thattransforms all of the information from an irregularly spaced network ofstations and imports the information on uniform cubical grids in afashion that is sufficiently accurate for the necessary predictions. ANyquist sampling technique can be utilized, which can be used along witha Barnes and/or Cressman sampling technique. This transformation isnecessary for the ingest process. From these functional flows a threatindex can be developed.

There is also a user interface flow. The user interface flow allows theuser to intercede and modify the operation of the EAS process. Forexample a user through the user interface can modify or eliminate faultyinput data or forecasts and fine-tune the threat index. Also the usercan input through the user interface additional parameterizedinformation such as the content of a chemical involved in a chemicalspill. The user may also make modifications due to knownclimatologically induced conditions that may have an effect onprediction capability and parameterization schemes.

In order to insure that only correct data from the Mesonet areincorporated into the EAS process, data from the Mesonet stations iscarefully examined. The data from each station is checked to insure thatthe values are within realistic ranges (level 1 check). The data is thencompared against values for the same station for previous times (level 2check) to insure that a single miss-transmission of data has notoccurred. The data is then compared with data from nearby stations toinsure that several miss-transmissions or contaminated data have notoccurred. The Mesonet ingest function then stores the data from theMesonet into a database for later retrieval by other parts of the EASprocess.

The National Weather Ingest function imports observations and globalscale numerical weather forecasts from the National Oceanographic anAtmospheric Administration and stores the information in a database forformulation of the forecast model.

The EAS process also utilizes various databases containing informationrelated to the natural environment such as terrain, land use, soilmoisture, soil temperature and trees to create the high spatial andtemporal resolution weather forecast. The EAS process also utilizesvarious databases containing information related to infrastructure andcritical facilities such as overhead power lines, electrical utilitysubstations, schools and hospitals. The combination of the high spatialand temporal resolution forecast and the various databases relating toinfrastructure and critical facilities gives the EAS process the abilityto create threat indicators that can assist logistical managers withinthe utility industry, emergency response industry, or other governmentagencies in the allocation of resources and manpower. The localizedweather prediction having maximized temporal and spatial resolutioncombined with other information can allow for example a utility industrylogistical manager to allocate resources in localized areas where icingis forecasted and trees are located adjacent power lines. The highresolution from the Mesonet combined with weather prediction models(WRF), allows for the prediction models to be more effectively tuned,due to the local maximized spatial and temporal data provided by theMesonet, to fit the local conditions.

This is accomplished by choosing from the many possible choices ofparameterization schemes based on the Mesonet data where thoseparameterizations schemes are such that when they are combined with WRFcan provide the best possible localized forecast. The present inventiondetermines which combinations of parameterization schemes produce thehighest spatial and temporal correlation factors for the application,for maximizing spatial and temporal resolution and for the region. Thisis accomplished by utilizing the forecasted weather conditions andobserved weather conditions from the real time localized sensors of theMesonet. The available parameterization scheme options are reflected inAppendix land a representative determined scheme is reflected inAppendix 2. Weather forecast maps, graphics and threat indexes can beproduced and provided to a logistical manager for allocating resourcesand manpower.

EAS can also provide a threat index for an event that is designed toidentify problem areas of interest that merits action or a closer watch.The threat index level can be determined by considering a combination ofthree factors including 1.) the type of asset; 2.) weather conditionsthat can potentially place the asset at risk; and 3.) special conditionsin the area that can potentially amplify the threat. In the case of anelectrical utility, the asset could be power lines or high tensiontowers. The weather conditions could be icing or high winds. The specialconditions could be trees hanging over the power lines. The threat indexcould be a level rating of 0 to 100 where 100 is the highest threatlevel. EAS determines at what geographic locations and what predictedtimes will all three of those conditions exist and attach to that anumber.

Given a high spatial and temporal resolution meteorological forecast ismade, the system combines the weather predictions with infrastructuredata and/or natural environment data of the area to create a threatlevel index and a graphical map presentation of any threats. As will beunderstood by one skilled in the art, certain weather conditionscombined with certain types of infrastructure can create emergencyconditions. The threat level index is a probabilistic tool that ratesareas in which certain infrastructure is likely to encounter certain,possibly hazardous conditions, and/or outages, which can result in ahigh probability of danger and/or service outage periods.

For example, in one embodiment of the present invention, the predictiveindicator system provides a threat level index for a weather event to anmunicipal electric utility company. Infrastructure data including thelocation of above-ground power lines and trees can be infrastructure andnatural inputs into the system. The system can then analyze thelikelihood that winds over a certain speed will occur and/or that icingwill occur in an area where both above-ground power lines and trees arepresent. Where it is determined that all three of these factors arelikely to overlap, a high threat level is assigned. Thus, an EmergencyManagement Center of an electric company can be notified and therefore,be able to decide where and when to martial manpower before the crisisor outage arises.

Referring to FIG. 5, an illustration of the NWS data ingest process isprovided. The National Weather Service (NWS) ingest process is designedto obtain the necessary observations and global-scale numerical weatherforecasts from the National Weather Service. The NWS ingest processbegins by removing any files from previous runs of the NWS ingestprocess and determining the date and time of the data to be ingested.The NWS ingest process then attempts to obtain the necessary globalmodel forecast data to initialize the local model. The NWS ingestprocess makes threes attempts to obtain the global model forecast datafrom the local source of NWS data (the Local Data Manager: LDM in FIG.5). If three unsuccessful attempts are made to obtain the global modelsforecast data locally, the NWS ingest process sends mail to theforecaster on duty notifying them and the failure and then makes threeattempts to obtain the global model data directly from the NationalWeather Service servers. If this fails the NWS ingest process againnotifies the forecaster on duty and resets the date and time to obtainan earlier run of the global model forecasts data. If this process failsonce the NWS process exits and notifies all forecasters of the failure.Given the global model forecast data the NWS ingest process creates thenecessary Mesoscale model boundary conditions naming the files with thedate and time of the initializations. The global model forecast data,boundary conditions and files with the correct parameterization schemesare passed to the Mesoscale forecast model and to a database where theinformation is stored for other processes. Parameterization schemes aretypically region and application dependent—the correct parameterizationschemes for a region in the Midwest, such as for example Missouri, wouldbe different from a coastal region, such as for example California, dueto the different general weather conditions experienced in suchdisparate regions.

Referring to FIG. 6, a functional flow of the Mesonet Data IngestProcess is illustrated. As noted in paragraphs 40 and 41 above the datafrom each of the sensor arrays are strategically placed based onregional characteristics to maximize the spatial and temporal resolutionfor the specific application. Further these sensor arrays are can beconnected to a commonly used utility industry network called SCADA.Other networks can be utilized without departing from the scope of thepresent Invention. Thus the data from the SCADA network is firstcollected at a SCADA central site where the data from all the sensorsare collected and sorted by time. The collected and sorted data from thesensors are then forwarded to EAS process central site. At userselectable intervals the Mesonet ingest process contacts the SCADAcentral site to determine what data is available. If the requested datais available it transferred from the SCADA central site to the EASprocess central site. If not, then three attempts are made to requestthe data from the SCADA central site. If after three attempts therequested data is not available email is sent to the forecaster on dutywarning of the failure. If the data is available, the raw Mesonet datais stored in a database and the Mesonet quality control program isforked from the Mesonet ingest process. Once the Mesonet quality controlprogram returns the quality controlled data and the quality controlflags are stored as a second entry in the Mesonet data database for eachdate time stamp and station. Each ingest cycle is assigned a date andtime stamp so that near real time data can be utilized along with themost recent trends and transitions.

Referring to FIG. 7 a, a functional flow of the Mesonet Quality ControlProcess is provided. As with other components of the EAS process, theMesonet quality control process begins by obtaining the date and time ofthe Mesonet data that is to be quality controlled. The data from a SCADAcentral site can optionally not have important information, such asinformation regarding the latitude, longitude and height above sea levelof a sensor, attached to the data transmitted from the sensor in orderto minimize the amount of information transmitted from each sensorpackage. In that case, the data from a sensor station preferablycontains the “name” of the sensor station and the weather data recordedby that sensor, allowing the EAS system to then determine the omittedinformation from a lookup table of sensor station names correlated withsuch latitude, longitude and height above sea level data. Once a matchis found the data record has the appropriate information attached.

Referring to FIG. 7 b, the first step in the Mesonet quality controlprocess is to check the data from each station to insure that the valuesare within realistic ranges. This is referred to in FIG. 7 a as a level1 check. The ranges of realistic values are based on both the season andtypical values suggested by the National Weather Service. Values thatare out of bounds are flagged as bad data by setting the flag to theamount the value was above or below the acceptable ranges.

Referring to FIG. 7 c, the next step in the Mesonet Quality controlprocess is a check for temporal continuity. If this station's data hasfailed a level 1 check, then a level 2 check of this station's data isnot conducted. In order to conduct a temporal continuity check, the datafrom each station is compared to values for the same station in 1minute, 5 minute, 15 minute and 60 minute increments in the past toinsure that a single miss-transmission of data has not occurred. This isreferred to as a level 2 quality control check in FIG. 7 a. As with alevel 1 check, if bad data is determined, it is flagged by setting thebad data flag to the amount of error found in the data.

Referring to FIG. 7 d, the next step in the Mesonet quality controlprocess is to test a station's data is representative. In this case eachstations data is compared to its neighbors' to determine a stations datais significantly different than near by stations. If this stations datahas failed a level 2 check, a level 3 check of this stations data is notconducted. This check is performed by removing each station in turn fromthe list of stations reporting. A Barnes objective analysis is thencomputed with the restricted data set. A bi-linear interpolation of thegridded data to the location of the station removed and the values fromthe interpolation is compared to the actual data at the station. If thestation is truly representative of the weather there will only be asmall difference between the interpolated data and the actual data. Thisis referred to as a level 3 check. As with the level 1 and level 2checks any stations with errors are flagged with the amount of errorfound in the stations data.

Referring to FIG. 8, a functional flow of the Mesonet Graphicspre-processor is provided. The advantage that the Mesonet data providesis the monitoring of the weather conditions at locations spaced closertogether than prior art monitoring stations. The greater number ofsensors spread over a smaller area provides a higher spatial andtemporal resolution of the existing weather and allows the Mesoscalemodel to start with a more accurate set of boundary conditions. So partof the solution provided by EAS to the question of how big the domain isand how to place stations is very dependent on the particular problemthat is trying to be solved. It is a time problem in the sense of howmuch is needed. It is also a spatial problem because EAS must determinehow much spatial coverage is needed or how far to go back to get enoughupwind or upstream data. So, the placement of the sensor stations forEAS is dependent in space and time on how much lead time is needed.There is a spatial and temporal resolution. The location of assets ofconcern is also a driver for placement of the sensor stations. Further,unique natural or land-use or infrastructure conditions can be a driveras well as known prediction problems. EAS has an optimization scheme toaddress the various needs. However, an increase in the number of sensorstations forming a local mesonet is not all that EAS provides. The EASsystem's strategic placement of sensor stations is also provided.

Strategic placement of a sensor station for the local mesonet can bedetermined by considering three primary parameters and they are—1.) thetype of weather threat/event of concern; 2.) the type and location ofthe asset that is potentially threatened; 3.) extraneous unique localconditions that may render an otherwise predictable environmentunpredictable or magnify the threat level of the weather threat/eventeven when the weather threat/event would have normally been a non-event.Item 1.), though considered, can be rendered of little or no effect ifeach sensor station placed has the entire suite of sensor types and thesensor station is optimally placed such that each sensor type will get agood reading. The stations also can have the ability to sample in thevertical. However, placement of a station for optimal reading of allsensor types will be overridden if the asset of concern is mostthreatened by a particular type of weather threat/event such thatplacement is skewed toward a placement that provides best sensingcapability for that particular type of weather threat/event that theasset is most endangered by.

For example, a power line may be most threatened by icing or high winds.Therefore, the most heavily weighted parameter can likely be the typeand location of the asset that is potentially threatened. Placement ofsensor stations are made to provide the best coverage for the assets ofconcern and placement of stations are skewed toward a placement thatallows for best sensing of a weather threat/event of most concern. Itemthree can be natural or man made obstructions or objects orclimatologically induced conditions that hinder normal predictivecapabilities or that can increase the threat level above what it wouldotherwise normally be. For example, a building may hinder an accuratereading of wind conditions. Traditional placement of sensor stationshave typically resulted in placement of sensor station in and aroundmajor airports because of there typical proximate to large metropolitanareas. If it is decided to install an EAS system in an region, thensensor stations can be installed strategically at various locations asdictated by the above parameters in order to form a local mesonetnetwork, which provides coverage for the assets of concern in theregion. The local mesonet can be implemented and layered in combinationwith other available data as discussed above.

There also may be areas of interest were EAS has determined an effectiveparameterization scheme in a particular region is not feasible thusactual data is needed thus driving the placement of sensor stations inthe local mesonet. EAS is designed with locations to fill in gaps whereinformation is needed to have an answer to or description of conditionsso that EAS can report what is actually happening on a regular basisbased on what is seen across the network in terms of how much longer,for example the icing event, is going to continue and how heavy theicing was going to be and how fast is it accumulating. The EAS systemalso allows for user input to make adjustments just based on userknowledge of current conditions or historical knowledge. Thus a user canalter the boundary layer parameterization scheme. There's a differentchoice for that particular combination. And so there's a list of these,all of these combinations that we, you know, have, there's this greatbig table that says if this is happening, then go pick this particularset of parameterization schemes.

To best assess the existing weather conditions, all of themeteorological parameters need to be seen in context, both spatially andtemporally. The Mesonet graphics pre-process places the Mesonet datarequested into the formats needed by the Mesonet graphics tools. TheMesonet data is presented in one of two major forms: The first is in theform of standard meteorological diagrams such as meteograms and stationmodels for web based graphics. The second form is standard GoogleEarth™KMZ files. The Mesonet graphics pre-processor sorts the Mesonet data bydate, time and station to create of list Mesonet station data to beplotted. Once the list of stations is created the Mesonet data iswritten out in an intermediate form that is readable by both standardweb-based graphics and GoogleEarth™

Referring to FIG. 9 a, a functional flow of the Forecast Model isprovided. In order for the Mesoscale meteorological model to create thehigh spatial and temporal resolution forecasts several independent datasets need to be combined in order to create the lower and lateralboundary conditions for the Mesoscale model. Some of these data sets canbe referred to as static data as they change only slowly. An example ofa static data set is the topography of the region over which theMesoscale model is to be run. A second example of a slowly varying butstatic (for the purposes of the Mesoscale model) data set is thevegetation and land use data. These data are organized by latitude andlongitude and available from a number of sources on latitudinal gridsthat do not match the grid used in the Mesoscale model.

In order to make use of this static data on the grids, the model needsto be a WRF model that allows placement of the static geographic data ona compatible grid of the WRF model by using the geogrid process in orderto convert for internal Mesonet model grid values. This creates anintermediate format file that is used by other processes in theMesoscale model. At the same time the lateral boundary conditions neededby the model to allow for weather systems to pass across the Mesoscalemodel are provided by the global scale forecasts from the NationalWeather Service. This data is transmitted in a compressed binary formthat needs to be unpacked before it can be used. The WRF model uses theUnGRIB process to unpack the binary formatted data into an intermediateform that can be used with the Mesoscale model. As with the static datathe global scale forecast data are not on the same grid as that of theMesoscale model. To convert the global scale forecast data into theboundary conditions needed by the Mesoscale model, the WRF processMetGRID combines the static geographic data and the global forecast datainto a single data set on the grid required by the Mesoscale model. Atthis point all the data needed by the Mesoscale model is on a consistentgrid but is not dynamically and kinematicaly consistent. In order torender this new data set consistent, the WRF process real is executed toinsure that all the dynamical and kinematic constraints are enforced.Given the dynamically and kinematically constrained data the Mesoscalemodel is run to create the high spatial and temporal resolutionforecasts needed.

The Mesoscale prediction model that generates high-resolutionmeteorological fields, which allows for localized Forecast fields, whichcan be combined with application specific geospatial data on clientinfrastructure land use and other data. Threat index maps can begenerated by application specific combination of meteorological andother data. Mesoscale observation network can be achieved by remotelylocated sensor suites that can be Commercial of the shelf sensor packagewith wireless communication and solar charged batteries or a customizedsuites. A Pre-existing digital broadband communication network, such asa SCADA network, can be leveraged. The system can provide a localizedHigh spatial and temporal resolution customized to a specificapplication that has Near real-time quality control and ingest ofMesoscale observed data. This data can be utilized in combination withthe FOSS Mesoscale prediction model with the FOSS Mesoscale modeloptimized for best forecast performance.

Referring to FIG. 9 b, an illustration of a three-dimensional renderingof forecast data is provided.

Referring to FIG. 9 c, an illustration of forecast data is provided. Inthis illustration, wind vectors can be seen, which relate wind directionand intensity across a region. The length, direction and placement ofthe arrows define the speed and direction of the wind at specificpositions. When combined, the wind vectors comprise a wind gradient.Very high wind speeds can be flagged as possible threat.

Referring to FIG. 9 d, an illustration of threat level data is provided.In this illustration, threat levels are shown across the state ofMissouri, with the highest threat levels being located in thesoutheastern corner of the state. Such an output from the EAS systemcould be analyzed by a forecaster, or be given to the Emergency ResponseManagement of a business or the government for their review. Based onsuch an illustration, such management could determine how to response tothe impending threat.

The EAS process as described above is an automated system with oversightby an experienced weather forecaster who can at their discretion alterthe flow of the process via a user interface. This allows the EASprocess to more faithfully determine the threat posed by a weather eventand to allow the EAS process to handle unexpected events such as ahazardous chemical spill. Every day a new Lead Forecaster can take overat 12UTC. At this point the lead forecaster verifies that the Mesonetdata and network are operating normally by monitoring the statusprovided by the user interface. Further the lead forecaster examines thelog files created by the previous lead forecaster for notes on theperformance of the system during the previous shift. To further verifythe performance of the EAS process, the Mesonet Quality control run logsand model run logs are examined to insure that no unexpected resultshave been produced. At this point the lead forecaster reaches a decisionpoint for the EAS process.

The data from the National Center for Environmental Prediction hasbecome available and the lead forecaster can make a decision as to whichconfiguration of the Mesoscale model to run. If the lead forecaster doesnot believe a significant weather event will occur in the nexttwenty-four hours or if the scale of the significant event can beaccurately forecast by a slightly lower resolution, a high spatial andtemporal resolution (9 km grid spacing) version of the Mesoscale modelis selected via the user interface and run by allowing the EAS processautomated software to run to completion. If on the other hand in theopinion of the lead forecaster a significant weather event will occur inthe next twenty-four hours a very high spatial and temporal resolution(1 km grid spacing) version of the Mesoscale model can be selected viathe user interface and run by restarting the EAS process with a the highresolution flag set. Further the lead forecaster can make the decisionas to whether to run multiple realizations of the Mesoscale modelutilizing different starting and boundary conditions in order to createthe most accurate assessment of the significant weather event. Byrerunning the model with varying starting and boundary conditions, thelead forecaster can analyze the variance in the prediction outcomes anddetermine a confidence level in the assessment of the weather.

In the event the lead forecaster decides that a significant weatherevent will occur in the next twenty-four hours requiring the very highresolution version of the Mesoscale model to be run, preferably the pasttwo hours of quality controlled data from the Mesonet is combined withthe data produced by real process (which may have been run withoutMesonet data incorporated) in the standard WRF initialization processvia WRF 3D-VAR. More or less Mesonet data may be incorporated, thoughmore data increases run time. WRF 3D-VAR creates a new set of lateraland lower boundary conditions that are markedly improved due to theaddition of the two hours of Mesonet data. In addition the leadforecaster may make contact with other forecasters associated with theEAS process for the independent verification of the event.

This contact can be provided via the user interface. Given theadditional forecast information, if merited, a warning can be sent tothe mail address of those parties that will be affected by thesignificant weather event. This message can also be transmitted via theuser interface. In addition the lead forecasters name and telephonenumber can be included as part of the email to allow the partiesaffected by the weather event to pose more detailed questions. As theMesoscale model creates the numerical weather forecast, the model outputis verified against Mesonet data later attained during the forecastedwindow to insure the Mesoscale model has not failed in some unknownmanner. This raises confidence in the results of the Mesoscale model. Asthe significant weather event unfolds the lead forecaster can contactparties affected, giving updates to the progress of the event, and canprovide an assessment of the threat posed by the weather event. Thethreat index described is computed by combining the relevant factors forthe parties affect into the geographically and temporally distributedthreat index.

If no significant weather event is predicted to occur with the nexttwenty four hours the lead forecaster can stand down and the EAS processis allowed to continue function without further intervention.

The various EAS system and process examples shown above illustrate anovel Emergency Management system. A user of the present invention maychoose any of the above embodiments, or an equivalent thereof, dependingupon the desired application. In this regard, it is recognized thatvarious forms of the subject invention could be utilized withoutdeparting from the spirit and scope of the present invention.

As is evident from the foregoing description, certain aspects of thepresent invention are not limited by the particular details of theexamples illustrated herein, and it is therefore contemplated that othermodifications and applications, or equivalents thereof, will occur tothose skilled in the art. It is accordingly intended that the claimsshall cover all such modifications and applications that do not departfrom the sprit and scope of the present invention.

Other aspects, objects and advantages of the present invention can beobtained from a study of the drawings, the disclosure and the appendedclaims.

APPENDIX I WRF Namelist Options Description of Namelist Variables

For WRF-NMM users, please see Chapter 5 of the WRF-NMM User's Guide forinformation on NMM specific settings(http://www.dtcenter.org/wrf-nmm/users)

 Note: variables followed by (max_dom) indicate that this variable needsto   be defined for the nests when max_dom > 1.  &time_control  run_days= 1, ; run time in days  run_hours  = 0, ; run time in hours  Note: ifit is more than 1 day, one may use both run_days and run_hours  or justrun_hours. e.g. if the total run length is 36 hrs, you may  set run_days= 1, and run_hours = 12, or run_days = 0, and run_hours = 36 run_minutes   = 0, ; run time in minutes  run_seconds   = 0, ; run timein seconds  start_year (max_dom)   = 2001, ; four digit year of startingtime  start_month (max_dom)   = 06, ; two digit month of starting time start_day (max_dom)   = 11, ; two digit day of starting time start_hour (max_dom)    = 12, ; two digit hour of starting time start_minute (max_dom)   = 00, ; two digit minute of starting time start_second (max_dom)   = 00, ; two digit second of starting time  Note: the start time is used to name the first wrfout file.   It alsocontrols the start time for nest domains, and the time to restart tstart (max_dom)   = 00,  ; FOR NMM: starting hour of the forecast end_year (max_dom)   = 2001, ; four digit year of ending time end_month (max_dom)    = 06, ; two digit month of ending time  end_day(max_dom)   = 12, ; two digit day of ending time  end_hour (max_dom)   =12, ; two digit hour of ending time  end_minute (max_dom)    = 00, ; twodigit minute of ending time  end_second (max_dom)    = 00, ; two digitsecond of ending time  It also controls when the nest domainintegrations end  All start and end times are used by real.exe.  Notethat one may use either run_days/run_hours etc. or end_year/month/day/hour etc. to control the length of  modelintegration. But run_days/run_hours  takes precedence over the endtimes.  Program real.exe uses start and end times only. interval_seconds   = 10800, ; time interval between incoming real data,which will be the interval  between the lateral boundary condition file input_from_file (max_dom)   = T, ; whether nested run will have inputfiles for domains other than 1  fine_input_stream (max_dom)   = 0, ;field selection from nest input for its initialization  0: all fieldsare used; 2: only static and time-varying, masked land  surface fieldsare used.  history_interval (max_dom)    = 60, ; history output fileinterval in minutes  frames_per_outfile (max_dom)   = 1, ; output timesper history output file, used to split output files   into smallerpieces  restart  = F,  ; whether this run is a restart run restart_interval    = 1440, ; restart output file interval in minutes io_form_history  = 2,   ; 2 = netCDF  io_form_restart = 2,   ; 2 =netCDF  io_form_input = 2,   ; 2 = netCDF  io_form_boundary   = 2,   ;netCDF format = 4,   ; PHD5 format = 5,   ; GRIB1 format frames_per_emissfile   = 12,  ; Number of times in each chemistryemission file.  io_style_emiss = 1,  ; Style to use for the chemistryemission files.   ; 0 = Do not read emissions from files.   ; 1 = Cyclebetween two 12 hour files (set  frames_per_emissfile=12)   ; 2 = Datedfiles with length set by frames_per_emissfile  debug_level = 0,   ;50,100,200,300 values give increasing printsTo choose between SI and WPS input to real:

 auxinput1_inname  = “met_em.d<domain>.<date>”    ; Input to real fromWPS = “wrf_real_input_em.d<domain>.<date>”  ; Input to real from SIOther output options:  auxhist2_outname  = “rainfall” ; file name forextra output; if not specified,  auxhist2_d<domain>_<date> will be used also note that to write variables in output other  than the historyfile requires Registry.EM file change  auxhist2_interval (max_dom)   =10,  ; interval in minutes  io_form_auxhist2 = 2,   ; output in netCDFAdditional ones when running 3DVAR:

 write_input = t,   ; write input-formatted data as output inputout_interval  = 180,  ; interval in minutes when writinginput-formatted data  input_outname  =‘wrf_3dvar_input_d<domain>_<date>’ ; you may change the output file name inputout_begin_y   = 0  inputout_begin_mo  = 0  inputout_begin_d   = 0 inputout_begin_h   = 3  inputout_begin_m  = 0  inputout_begin_s   = 0 inputout_end_y  = 0  inputout_end_mo  = 0  inputout_end_d  = 0 inputout_end_h  = 12  inputout_end_m   = 0  inputout_end_s  = 0 ; theabove shows that the input-formatted data are output   starting fromhour 3 to hour 12 in 180 min interval.  &domains  time_step = 60, ; timestep for integration in integer seconds   recommend 6*dx (in km) fortypical real-data cases  time_step_fract_num   = 0, ; numerator forfractional time step  time_step_fract_den   = 1, ; denominator forfractional time step   Example, if you want to use 60.3 sec as your timestep,   set time_step = 60, time_step_fract_num = 3, and  time_step_fract_den = 10  max_dom = 1, ; number of domains - set itto > 1 if it is a nested run  s_we (max_dom)  = 1, ; start index in x(west-east) direction (leave as is)  e_we (max_dom)   = 91, ; end indexin x (west-east) direction (staggered dimension)  s_sn (max_dom)  = 1, ;start index in y (south-north) direction (leave as is)  e_sn (max_dom) = 82, ; end index in y (south-north) direction (staggered dimension) s_vert (max_dom)    = 1, ; start index in z (vertical) direction (leaveas is)  e_vert (max_dom)   = 28, ; end index in z (vertical) direction(staggered dimension)   Note: this refers to full levels includingsurface and top   vertical dimensions need to be the same for all nests  Note: most variables are unstaggered (= staggered dim - 1)  dx(max_dom)  = 10000,   ; grid length in x direction, unit in meters  dy(max_dom)  = 10000,   ; grid length in y direction, unit in meters  ztop(max_dom)  = 19000.   ; used in mass model for idealized cases  grid_id(max_dom)  = 1, ; domain identifier  parent_id (max_dom)   = 0,  ; id ofthe parent domain  i_parent_start (max_dom)   = 0,   ; starting LLCI-indices from the parent domain  j_parent_start (max_dom)   = 0,   ;starting LLC J-indices from the parent domain  parent_grid_ratio(max_dom) = 1,   ; parent-to-nest domain grid size ratio: for real-datacases  the ratio has to be odd; for idealized cases,  the ratio can beeven if feedback is set to 0.  parent_time_step_ratio (max_dom) = 1,   ;parent-to-nest time step ratio; it can be different  from theparent_grid_ratio  feedback = 1,   ; feedback from nest to its parentdomain; 0 = no feedback  smooth_option  = 0 ; smoothing option forparent domain, used only with feedback   option on. 0: no smoothing; 1:1-2-1 smoothing; 2: smoothing-desmoothingNamelist variables specifically for the WPS input for real:

 num_metgrid_levels  = 27   ; number of vertical levels of 3dmeteorological fields coming   ; from WPS metgrid program  interp_type =1   ; vertical interpolation   ; 1 = linear in pressure   ; 2 = linearin log(pressure)  lagrange_order  = 1   ; vertical interpolation order  ; 1 = linear   ; 2 = quadratic  zap_close_levels   = 500   ; ignoreisobaric level above surface if delta p (Pa) < zap_close_levels lowest_lev_from_sfc  = .false. ; place the surface value into thelowest eta location   ; T = use surface value as lowest eta (u,v,t,q)  ; F = use traditional interpolation  force_sfc_in_vinterp  = 1   ; usethe surface level as the lower boundary when interpolating   ; throughthis many eta levels   ; 0 = perform traditional trapping interpolation  ; n = first n eta levels directly use surface level  p_top_requested  = 5000  ; p_top (Pa) to use in the modelUsers may explicitly define full eta levels. Given are two distributionsfor 28 and 35 levels. The numberof levels must agree with the number of eta surfaces allocated (e_vert).Users may alternatively requestonly the number of levels (with e_vert), and the real program willcompute values. The computation assumesa known first several layers, then generates equi-height spaced levelsup to the top of the model.

eta_levels  = 1.000, 0.990, 0.978, 0.964, 0.946, 0.922, 0.894, 0.860,0.817, 0.766, 0.707, 0.644, 0.576, 0.507, 0.444, 0.380, 0.324, 0.273,0.228, 0.188, 0.152, 0.121, 0.093, 0.069, 0.048, 0.029, 0.014, 0.000,eta_levels  = 1.000, 0.993, 0.983, 0.970, 0.954, 0.934, 0.909, 0.880,0.845, 0.807, 0.765, 0.719, 0.672, 0.622, 0.571, 0.520, 0.468, 0.420,0.376, 0.335, 0.298, 0.263, 0.231, 0.202, 0.175, 0.150, 0.127, 0.106,0.088, 0.070, 0.055, 0.040, 0.026, 0.013, 0.000Namelist variables for controlling the specified moving nest:

  Note that this moving nest option needs to be activated at the compiletime by adding -DMOVE_NESTS   to the ARCHFLAGS. The maximum number ofmoves, max_moves, is set to 50   but can be modified in source code fileframe/module_driver_constants.F.  num_moves  = 4   ; total number ofmoves  move_id = 2,2,2,2, ; a list of nest domain id's, one per move move_interval = 60,120,150,180,  ; time in minutes since the start ofthis domain  move_cd_x = 1,1,0,−1,; the number of parent domain gridcells to move in i direction  move_cd_y = 1,0,−1,1,; the number ofparent domain grid cells to move in j direction  positive is to move inincreasing i and j direction, and  negative is to move in decreasing iand j direction.  0 means no move. The limitation now is to move only 1grid cell  at each move.Namelist variables for controlling the automatic moving nest:

  Note that this moving nest option needs to be activated at the compiletime by adding -DMOVE_NESTS   and -DVORTEX_CENTER to the ARCHFLAGS. Thisoption uses an mid-  level vortex following algorthm to   determine thenest move. This option is experimental.  vortex_interval  = 15  ; howoften the new vortex position is computed  max_vortex_speed   = 40  ;used to compute the search radius for the new vortex position corral_dist = 8  ; how many coarse grid cells the moving nest isallowed to get   near the mother domain boundary  &physics  Note: eventhe physics options can be different in different nest domains,  cautionmust be used as what options are sensible to use  chem_opt  = 0,  ;chemistry option - not yet available mp_physics (max_dom)      microphysics option = 0, no microphysics = 1, Kessler scheme = 2,Lin et al. scheme = 3, WSM 3-class simple ice scheme = 4, WSM 5-classscheme = 5, Ferrier (new Eta) microphysics = 6, WSM 6-class graupelscheme = 8, Thompson et al. scheme = 98, NCEP 3-class simple ice scheme(to be removed) = 99, NCEP 5-class scheme (to be removed) For non-zeromp_physics options, to keep Qv .GE. 0, and to set the other moisturefields .LT. a critcal value to zero mp_zero_out  = 0,  ; no actiontaken, no adjustment to any moist field = 1,  ; except for Qv, all othermoist arrays are set to zero  ; if they fall below a critical value = 2, ; Qv is .GE. 0, all other moist arrays are set to zero  ; if they fallbelow a critical value  mp_zero_out_thresh   = 1.e−8  ; critical valuefor moist array threshold, below which  ; moist arrays (except for Qv)are set to zero (kg/kg)  ra_lw_physics (max_dom)       longwaveradiation option = 0, no longwave radiation = 1, rrtm scheme = 3, camscheme  also must set levsiz, paerlev, cam_abs_dim1/2 (see below) = 99,GFDL (Eta) longwave (semi-supported)  also must use co2tf = 1 for ARW ra_sw_physics (max_dom)        shortwave radiation option = 0, noshortwave radiation = 1, Dudhia scheme = 2, Goddard short wave = 3, camscheme  also must set levsiz, paerlev, cam_abs_dim1/2 (see below) = 99,GFDL (Eta) longwave (semi-supported)  also must use co2tf = 1 for ARW radt (max_dom)   = 30,  ; minutes between radiation physics calls recommend 1 min per km of dx (e.g. 10 for 10 km)  nrads (max_dom)   =FOR NMM: number of fundamental timesteps between   calls to shortwaveradiation; the value   is set in Registry.NMM but is overridden   bynamelist value; radt will be computed   from this.  nradl (max_dom)    =FOR NMM: number of fundamental timesteps between   calls to longwaveradiation; the value   is set in Registry.NMM but is overridden   bynamelist value.  co2tf  CO2 transmission function flag only for GFDLradiation = 0, read CO2 function data from pre-generated file = 1,generate CO2 functions internally in the forecast  ra_call_offset radiation call offset = 0 (no offset), =−1 (old offset)  cam_abs_freq_s = 21600 CAM clearsky longwave absorption calculation frequency (recommended minimum value to speed scheme up)  levsiz = 59 for CAMradiation input ozone levels  paerlev  = 29 for CAM radiation inputaerosol levels  cam_abs_dim1   = 4 for CAM absorption save array cam_abs_dim2   = e_vert for CAM 2nd absorption save array sf_sfclay_physics (max_dom)      surface-layer option (oldbl_sfclay_physics option) = 0, no surface-layer = 1, Monin-Obukhovscheme = 2, Monin-Obukhov (Janjic) scheme = 3, NCEP Global ForecastSystem scheme  sf_surface_physics (max_dom)      land-surface option(old bl_surface_physics option) = 0, no surface temp prediction = 1,thermal diffusion scheme = 2, Noah land-surface model = 3, RUCland-surface model  bl_pbl_physics (max_dom)      boundary-layer option= 0, no boundary-layer = 1, YSU scheme = 2, Mellor-Yamada-Janjic TKEscheme = 3, NCEP Global Forecast System scheme = 99, MRF scheme (to beremoved)  bldt (max_dom)    = 0,   ; minutes between boundary-layerphysics calls  nphs (max_dom)    = FOR NMM: number of fundamentaltimesteps between   calls to turbulence and microphysics;   the value isset in Registry.NMM but is   overridden by namelist value; bldt will  be computed from this.  cu_physics (max_dom)       cumulus option = 0,no cumulus = 1, Kain-Fritsch (new Eta) scheme = 2, Betts-Miller-Janjicscheme = 3, Grell-Devenyi ensemble scheme = 4, SimplifiedArakawa-Schubert scheme = 99, previous Kain-Fritsch scheme  cudt  = 0, ; minutes between cumulus physics calls  ncnvc (max_dom)    = FOR NMM:number of fundamental timesteps between   calls to convection; the valueis set in Registry.NMM   but is overridden by namelist value; cudt willbe   computed from this.  tprec (max_dom)   = FOR NMM: number of hoursin precipitation bucket  theat (max_dom)   = FOR NMM: number of hours inlatent heating bucket  tclod (max_dom)   = FOR NMM: number of hours incloud fraction average  trdsw (max_dom)    = FOR NMM: number of hours inshort wave buckets  trdlw (max_dom)   = FOR NMM: number of hours in longwave buckets  tsrfc (max_dom)   = FOR NMM: number of hours in surfaceflux buckets  pcpflg (max_dom)    = FOR NMM: logical switch forprecipitation assimilation  isfflx = 1,  ; heat and moisture fluxes fromthe surface   (only works for sf_sfclay_physics = 1)   1 = with fluxesfrom the surface   0 = no flux from the surface  ifsnow = 0,   ;snow-cover effects   (only works for sf_surface_physics = 1)   1 = withsnow-cover effect   0 = without snow-cover effect  icloud = 1, ; cloudeffect to the optical depth in radiation   (only works for ra_sw_physics= 1 and ra_lw_physics =  1)   1 = with cloud effect   0 = without cloudeffect  swrad_scat  = 1.  ; scattering tuning parameter (default 1. is1.e−5 m2/kg)  surface_input_source   = 1,  ; where landuse and soilcategory data come from:   1 = SI/gridgen   2 = GRIB data from anothermodel (only possible    (VEGCAT/SOILCAT are in wrf_real_input_em filesfrom SI)  num_soil_layers   = 5,  ; number of soil layers in landsurface model   = 5: thermal diffusion scheme   = 4: Noah landsurfacemodel   = 6: RUC landsurface model  ucmcall = 0,  ; activate urbancanopy model (in Noah LSM only) (0=no, 1=yes)  maxiens  = 1,  ;Grell-Devenyi only  maxens  = 3,  ; G-D only  maxens2  = 3,   ; G-D only maxens3  = 16   ; G-D only  ensdim  = 144   ; G-D only   These arerecommended numbers. If you would like to use   any other number,consult the code, know what you are doing.  seaice_threshold  = 271  ;tsk < seaice_threshold, if water point and 5-layer slab  ; scheme, setto land point and permanent ice; if water point  ; and Noah scheme, setto land point, permanent ice, set temps  ; from 3 m to surface, and setsmois and sh2o  sst_update = 0  ; time-varying sea-surface temp (0=no,1=yes). If selected real  ; puts SST and VEGFRA in wrflowinp_d01 file,and wrf updates these from it  ; at same interval as boundary file. Toread this, the time- control  ; namelist must includeauxinput5_interval, auxinput5_end_h, and  ; auxinput5_inname =“wrflowinp_d<domain>”  &fdda  grid_fdda (max_dom)   = 1  ; grid-nudgingfdda on (=0 off) for each domain  gfdda_inname  = “wrffdda_d<domain>” ;defined name in real  gfdda_interval_m (max_dom)   = 360  ; timeinterval (min) between analysis times  gfdda_end_h (max_dom)    = 6  ;time (h) to stop nudging after start of forecast  io_form_gfdda  = 2  ;analysis data io format (2 = netCDF)  fgdt (max_dom)   = 0  ;calculation frequency (minutes) for grid-nudging (0=every step) if_no_pbl_nudging_uv (max_dom)   = 0  ; 0= no nudging of u and v in thepbl, 1=nudging in the pbl  if_no_pbl_nudging_t (max_dom) = 0  ; 0= nonudging of temp in the pbl, 1=nudging in the pbl  if_no_pbl_nudging_q(max_dom) = 0  ; 0= no nudging of qvapor in the pbl, 1=nudging in thepbl  if_zfac_uv (max_dom)   = 0  ; 0= nudge u and v all layers, 1 =limit nudging to levels above k_zfac_uv  k_zfac_uv (max_dom)   = 10  ;10=model level below which nudging is switched off for u and v if_zfac_t (max_dom)  = 0  ; 0= nudge temp all layers, 1 = limit nudgingto levels above k_zfac_t  k_zfac_t (max_dom)  = 10  ; 10=model levelbelow which nudging is switched off for temp  if_zfac_q (max_dom)   = 0 ; 0= nudge qvapor all layers, 1 = limit nudging to levels abovek_zfac_q  k_zfac_q (max_dom)   = 10  ; 10=model level below whichnudging is switched off for qvapor  guv (max_dom)  = 0.0003  ; nudgingcoefficient for u and v (sec−1)  gt (max_dom)  = 0.0003  ; nudgingcoefficient for temp (sec−1)  gq (max_dom)   = 0.0003  ; nudgingcoefficient for qvapor (sec−1)  if_ramping = 0  ; 0= nudging ends as astep function, 1 = ramping nudging down at end of period  dtramp_min  =60.0  ; time (min) for ramping function, 60.0=ramping starts at lastanalysis time,         −60.0=ramping ends at last analysis timeThe following are for observation nudging:

 obs_nudge_opt (max_dom)   = 1   ; obs-nudging fdda on (=0 off) for eachdomain  also need to set auxinput11_interval and auxinput11_end_h  intime_control namelist  max_obs  = 150000  ; max number of observationsused on a domain during any  given time window  fdda_start = 0  ; obsnudging start time in minutes  fdda_end  = 180  ; obs nudging end timein minutes  obs_nudge_wind (max_dom)   = 1  ; whether to nudge wind: (=0off)  obs_coef_wind  = 6.E−4,  ; nudging coefficient for wind, unit: s−1 obs_nudge_temp   = 1  ; whether to nudge temperature: (=0 off) obs_coef_temp  = 6.E−4,  ; nudging coefficient for temperature, unit:s−1  obs_nudge_mois  = 1  ; whether to nudge water vapor mixing ratio:(=0 off)  obs_coef_mois  = 6.E−4,  ; nudging coefficient for water vapormixing ratio, unit: s−1  obs_nudge_pstr  = 0  ; whether to nudge surfacepressure (not used)  obs_coef_pstr  = 0.  ; nudging coefficient forsurface pressure, unit: s−1 (not used)  obs_rinxy  = 200.,  ; horizonalradius of influence in km  obs_rinsig  = 0.1,  ; vertical radius ofinfluence in eta  obs_twindo  = 40,  ; half-period time window overwhich an observation  will be used for nudging  obs_npfi = 10,  ; freqin coarse grid timesteps for diag prints  obs_ionf = 2  ; freq in coarsegrid timesteps for obs input and err calc  obs_idynin  = 0  ; fordynamic initialization using a ramp-down function to gradually  turn offthe FDDA before the pure forecast (=1 on)  obs_dtramp  = 40  ; timeperiod in minutes over which the nudging is ramped down  from one tozero.  obs_ipf_in4dob   = .true.  ; print obs input diagnostics(=.false. off)  obs_ipf_errob  = .true.  ; print obs error diagnostics(=.false. off)  obs_ipf_nudob   = .true.  ; print obs nudge diagnostics(=.false. off)  /  &dynamics  dyn_opt  = 2,  ; dynamical core option:advanced research WRF core (Eulerian mass)  rk_ord = 3,;time-integration scheme option:  2 = Runge-Kutta 2nd order  3 =Runge-Kutta 3rd order  diff_opt = 0,  ; turbulence and mixing option:  0= no turbulence or explicit   spatial numerical filters (km_opt ISIGNORED).  1 = evaluates 2nd order   diffusion term on coordinatesurfaces.   uses kvdif for vertical diff unless PBL option   is used.may be used with km_opt = 1 and 4.   (= 1, recommended for real-datacase when grid distance < 10 km)  2 = evaluates mixing terms in  physical space (stress form) (x,y,z).   turbulence parameterization ischosen   by specifying km_opt.  km_opt  = 1,  ; eddy coefficient option 1 = constant (use khdif kvdif)  2 = 1.5 order TKE closure (3D)  3 =Smagorinsky first order closure (3D)   Note: option 2 and 3 are notrecommended for DX > 2 km  4 = horizontal Smagorinsky first orderclosure   (recommended for real-data case when grid distance < 10 km) damp_opt = 0,  ; upper level damping flag  0 = without damping  1 =with diffusive damping, maybe used for real-data cases   (dampcoefnondimensional ~0.01-0.1)  2 = with Rayleigh damping (dampcoef inversetime scale [1/s] e.g. .003;   not for real-data cases)  diff_6th_opt =0,  ; 6th-order numerical diffusion  0 = no 6th-order diffusion(default)  1 = 6th-order numerical diffusion  2 = 6th-order numericaldiffusion but prohibit up-gradient diffusion  diff_6th_factor  = 0.12, ; 6th-order numerical diffusion non-dimensional rate (max value 1.0  corresponds to complete removal of 2dx wave in one timestep)  dampcoef(max_dom)   = 0.,  ; damping coefficient (see above)  zdamp (max_dom)  = 5000.,  ; damping depth (m) from model top  w_damping  = 0,  ;vertical velocity damping flag (for operational use)  0 = withoutdamping  1 = with damping  base_temp  = 290.,  ; real-data, em ONLY,base sea-level temp (K)  base_pres = 10{circumflex over ( )}5  ;real-data, em ONLY, base sea-level pres (Pa), DO NOT CHANGE  base_lapse= 50.,  ; real-data, em ONLY, lapse rate (K), DO NOT CHANGE  khdif(max_dom)  = 0,  ; horizontal diffusion constant (m{circumflex over( )}2/s)  kvdif (max_dom) = 0,  ; vertical diffusion constant(m{circumflex over ( )}2/s)  smdiv (max_dom)  = 0.1,   ; divergencedamping (0.1 is typical)  emdiv (max_dom)  = 0.01,  ; external-modefilter coef for mass coordinate model (0.01 is typical for real-datacases)  epssm (max_dom)  = .1, ; time off-centering for vertical soundwaves  non_hydrostatic (max_dom)  = .true.,  ; whether running the modelin hydrostatic or non-hydro mode  pert_coriolis (max_dom)  = .false.,  ;Coriolis only acts on wind perturbation (idealized) mix_full_fields(max_dom)  = .true., ; used with diff_opt = 2; value of“.true.” is recommended, except for highly idealized numerical tests;damp_opt must not be 1  if “.true.” is chosen. .false. means subtract1−d base-state profile before mixing  tke_drag_coefficient(max_dom)  =0.,  ; surface drag coefficient (Cd,  dimensionless) for diff_opt=2 only tke_heat_flux(max_dom)   = 0.,  ; surface thermal flux (H/(rho*cp), Km/s) for diff_opt=2 only  h_mom_adv_order (max_dom)   = 5,  ; horizontalmomentum advection order (5=5th, etc.)  v_mom_adv_order (max_dom)   = 3, ; vertical momentum advection order  h_sca_adv_order (max_dom)  = 5,  ;horizontal scalar advection order  v_sca_adv_order (max_dom)  = 3,  ;vertical scalar advection order  pd_moist  = F  ; positive definiteadvection of moisture  pd_scalar  = F  ; positive definite advection ofscalars  pd_chem  = F  ; positive definite advection of chem variables pd_tke = F  ; positive definite advection of tke  time_step_sound(max_dom)  = 4 /  ; number of sound steps per time-step (0=setautomatically)  (if using a time_step much larger than 6*dx (in km), proportionally increase number of sound steps - also  best to use evennumbers)  &bdy_control  spec_bdy_width  = 5,  ; total number of rows forspecified boundary value nudging  spec_zone = 1,  ; number of points inspecified zone (spec b.c. option)  relax_zone = 4,  ; number of pointsin relaxation zone (spec b.c. option)  specified (max_dom)  = .false., ;specified boundary conditions (only for domain 1)  the above 4 are usedfor real-data runs  periodic_x (max_dom)  = .false., ; periodic boundaryconditions in x direction  symmetric_xs (max_dom)  = .false., ;symmetric boundary conditions at x start (west)  symmetric_xe (max_dom) = .false., ; symmetric boundary conditions at x end (east)  open_xs(max_dom)  = .false., ; open boundary conditions at x start (west) open_xe (max_dom)  = .false., ; open boundary conditions at x end(east)  periodic_y (max_dom)   = .false., ; periodic boundary conditionsin y direction  symmetric_ys (max_dom)   = .false., ; symmetric boundaryconditions at y start (south)  symmetric_ye (max_dom)   = .false., ;symmetric boundary conditions at y end (north)  open_ys (max_dom)  =.false., ; open boundary conditions at y start (south)  open_ye(max_dom)  = .false., ; open boundary conditions at y end (north) nested (max_dom)   = .false., ; nested boundary conditions (inactive)&namelist_quilt This namelist record controls asynchronized I/O for MPIapplications.

 nio_tasks_per_group   = 0,   default value is 0: no quilting; > 0quilting I/O  nio_groups  = 1,   default 1, don't change  miscelleneousin &domains:  tile_sz_x = 0,   ; number of points in tile x direction tile_sz_y = 0,   ; number of points in tile y direction         can bedetermined automatically  numtiles = 1,   ; number of tiles per patch(alternative to above two items)  nproc_x = −1,  ; number of processorsin x for decomposition  nproc_y = −1,  ; number of processors in y fordecomposition  −1: code will do automatic decomposition  >1: for both:will be used for decomposition

APPENDIX II Actual Choice of Parameters for WRF

&time_control run_days   = 0, run_hours   = 9, run_minutes   = 0,run_seconds   = 0, start_year  = 2006, 2006, 2006, start_month   = 07, 07,  07, start_day   = 19,  19,  19, start_hour  = 18,  18,  18,start_minute   = 00,  00,  00, start_second   = 00,  00,  00, end_year = 2006, 2006, 2006, end_month    = 07,  07,  07, end_day  = 20,  20, 20, end_hour  = 03,  03,  03, end_minute   = 00,  00,  00, end_second  = 00,  00,  00, interval_seconds    = 10800 input_from_file   =.true.,.true.,.true., history_interval   = 10,  10,  10,frames_per_outfile    = 1000, 1000, 1000, restart = .false.,restart_interval  = 5000, io_form_history   = 2 io_form_restart   = 2io_form_input   = 2 io_form_boundary     = 2 debug_level  = 0 / &domainstime_step  = 20, time_step_fract_num    = 0, time_step_fract_den   = 1,max_dom   = 2, s_we  = 1,  1,  1, e_we  = 72,  100,  82, s_sn  = 1,  1, 1, e_sn  = 72,  91,  85, s_vert  = 1,  1,  1, e_vert  = 41,  41,  41,num_metgrid_levels    = 40, dx = 9000, 3000,  1000, dy = 9000, 3000, 1000, grid_id  = 1,  2,  3, parent_id  = 0,  1,  2, i_parent_start   =1,  30,  62, j_parent_start   = 1,  26,  25, parent_grid_ratio   = 1, 3,  3, parent_time_step_ratio    = 1,  3,  3, feedback  = 1,smooth_option   = 0 / &physics mp_physics   = 8,  8,  8, ra_lw_physics  = 1,  1,  1, ra_sw_physics    = 2,  2,  2, radt = 30,  30,  30,sf_sfclay_physics    = 3,  3,  3, sf_surface_physics     = 3,  3,  3,bl_pbl_physics   = 1  1,  1, bldt = 1,  1,  1, cu_physics   = 1,  1,  0,cudt  = 2,  2,  2, isfflx = 1, ifsnow   = 0, icloud  = 1,surface_input_source    = 2, num_soil_layers    = 6, ucmcall  = 0,mp_zero_out   = 0, maxiens  = 1, maxens  = 3, maxens2   = 3, maxens3   =16, ensdim   = 144, / &fdda / &dynamics w_damping    = 0, diff_opt  = 1,km_opt   = 4, diff_6th_opt  = 0, diff_6th_factor   = 0.12, base_temp  =290. damp_opt  = 0, zdamp   = 5000.,  5000.,  5000., dampcoef  = 0.01, 0.01,  0.01 khdif = 0,  0,  0, kvdif = 0,  0,  0, non_hydrostatic   =.true., .true., .true., pd_moist    = .false., .false., .false.,pd_scalar   = .false., .false., .false., / &bdy_control spec_bdy_width   = 5, spec_zone    = 1, relax_zone    = 4, specified    = .true.,.false.,.false., nested   = .false., .true., .true., / &grib2 /&namelist_quilt nio_tasks_per_group = 0, nio_groups = 1, /

1. A system for predicting weather-related threats, comprising: anetwork meteorological weather monitoring stations having a plurality ofmonitoring stations communicably linked over a wide area network to acentral server where each of said plurality of monitoring stationsoperable to transmit sensor data to said central server and where eachof said plurality of monitoring stations are geographically place basedon a type of weather threat parameter, a type of asset parameter, and alocal condition parameter thereby forming a local Mesonet; a computingsystem communicably linked to said central server over said wide arenetwork and having an initialization module adapted for initializing theboundary conditions for a EAS model domain, where said initializationmodule has a pre-processing sub-module including a Geo-Grid functionoperable to extract data from a geographic database to define the modeldomain lower boundary condition, an UnGrib function operable to extractdata imported from national weather service to define lateral boundaryconditions, and a Metgrid function operable to transform data from theUnGrib and the Geo-Grid for the EAS model domain; said computing systemfurther comprising an ingestion module adapted for ingesting initial EASmodel domain boundary conditions and combining with Local Mesonet to getWeather Research Forecasting model by importing Local Mesonet Data fromthe Local Mesonet and transformed by an ObsGrid sampling function intouniform data grid and storing into MetGrid; said computing systemfurther comprising a parameterization scheme module adapted forselecting and tuning a parameterization scheme based at least partiallyon Local Mesonet Data; and said computing system further comprising aPrediction module adapted for generating weather prediction data basedon the Weather Research Forecasting model and outputting the weatherprediction data to a user interface adapted for operator viewing.
 2. Thesystem for predicting weather related events as recited in claim 1,where said computing system further comprises a Threat Index moduleadapted to assign a Threat Index value based on type of asset, weatherthreat for asset; and local conditions and further adapted to presentthe Threat Index value in graphical form to the user interface.
 3. Thesystem for prediction weather related events as recited in claim 2,where the User Interface has an input device adapted to allow theoperator to input modification data to modify results of theinitialization module and the ingestion module based on historical data,recent trends and conditions known by the operator.
 4. The system forprediction of weather related events as recited in claim 2, where saidlocal conditions include natural environment and infrastructure data. 5.The system for prediction of weather related events as recited in claim4, wherein infrastructure data further includes land topology.
 6. Thesystem for prediction of weather related events as recited in claim 4,wherein infrastructure data further includes population density data,where said threat level index indicates the probability that winds willspread a harmful agent toward a location with a high population density.7. The system for prediction of weather related events as recited inclaim 4, wherein infrastructure data includes flammable substancelocation data, and where said threat level index indicates theprobability that winds will spread a fire toward a location which maycontain the flammable substance.
 8. The system for prediction of weatherrelated events as recited in claim 1, where said computing systemfurther comprises a Quality Control module adapted to monitor data fromLocal Mesonet stations and National Weather Service for out of toleranceconditions.
 9. The system for prediction of weather related events asrecited in claim 1, where said wide area network includes a SCADAnetwork adapted to communicably link said plurality of monitoringstations to a central SCADA server.
 10. The system for prediction ofweather related events as recited in claim 9, where said SCADA networkcomprises a plurality of substations communicably linked to themonitoring stations, where each substation is communicably linked tosaid central SCADA server.
 11. A method for predicting weather-relatedthreats, comprising the steps of: providing a network meteorologicalweather monitoring stations by placing plurality of monitoring stationsgeographically based on a type of weather threat parameter, a type ofasset parameter, and a local condition parameter thereby forming a localMesonet and communicably linking said plurality of monitoring stationsover a wide area network to a central server; transmitting sensor datafrom each of said plurality of monitoring stations to said centralserver and where each of said; initializing the boundary conditions fora EAS model domain using a computing system having an initializationmodule adapted for initializing the boundary conditions for the EASmodel domain; pre-processing medium scale forecast data with apre-processing sub-module of said initialization module including aGeo-Grid function and an UnGrib function and a Metgrid function;extracting data from a geographic database using the Geo-Grid functionto define the model domain lower boundary condition; extracting dataimported from national weather service using the UnGrib function todefine lateral boundary conditions; transforming data from the UnGriband the Geo-Grid for the EAS model domain using the Metgrid function;Ingesting initial EAS model domain boundary conditions and combiningwith Local Mesonet to get Weather Research Forecasting model using theingestion module of the computing system and importing Local MesonetData from the Local Mesonet and transformed by an ObsGrid samplingfunction into uniform data grid and storing into MetGrid; selecting andtuning a parameterization scheme based at least partially on LocalMesonet Data using the parameterization scheme module of the computingsystem; and generating weather prediction data based on the WeatherResearch Forecasting model and outputting the weather prediction data toa user interface adapted for operator viewing using a Prediction moduleof the computing system.
 12. The method for predicting weather relatedevents as recited in claim 11, where said computing system furthercomprises a Threat Index module adapted to assign a Threat Index valuebased on type of asset, weather threat for asset; and local conditionsand further adapted to present the Threat Index value in graphical formto the user interface.
 13. The method for predicting weather relatedevents as recited in claim 12, where the User Interface has an inputdevice adapted to allow the operator to input modification data andfurther comprising the step of modifying results of the initializationmodule and the ingestion module based on historical data, recent trendsand conditions known by the operator.
 14. The method for predicting ofweather related events as recited in claim 12, where said localconditions include natural environment and infrastructure data.
 15. Themethod for predicting of weather related events as recited in claim 14,wherein infrastructure data further includes land topology.
 16. Themethod for predicting of weather related events as recited in claim 14,wherein infrastructure data further includes population density data,where said threat level index indicates the probability that winds willspread a harmful agent toward a location with a high population density.17. The method for predicting of weather related events as recited inclaim 14, wherein infrastructure data includes flammable substancelocation data, and where said threat level index indicates theprobability that winds will spread a fire toward a location which maycontain the flammable substance.
 18. The method for predicting ofweather related events as recited in claim 11, further comprising thestep of monitoring with a Quality Control module of the computing systemdata from Local Mesonet stations and National Weather Service anddetecting out of tolerance conditions.
 19. The method for predicting ofweather related events as recited in claim 11, further comprising thestep of linking said plurality of monitoring stations to a central SCADAserver using the wide area network including a SCADA network.
 20. Themethod for predicting of weather related events as recited in claim 19,where said SCADA network comprises a plurality of substationscommunicably linked to the monitoring stations, where each substation iscommunicably linked to said central SCADA server.
 21. A system forpredicting weather-related threats, comprising: an electrical utilityinfrastructure having a SCADA network adapted for monitoring andreporting infrastructure status including a plurality of substationsdispersed throughout a region serviced by the electrical utilityinfrastructure, where said substations including a transceiver moduleoperable to receive and transmit infrastructure status transmissions,and where said SCADA network includes a central server communicablylinked to said plurality of substations and adapted to receive andprocess infrastructure status transmissions transmitted from theplurality of substations; a local Mesonet including a plurality ofweather monitoring stations dispersed throughout the region serviced bythe electrical utility infrastructure, where each weather monitoringstation including weather condition sensors and transmitters adapted totransmit to the SCADA network data representative of the weatherconditions sensed by the weather condition sensors; said central servercommunicably linked to a wide area network and adapted to transmit saiddata representative of the weather conditions over said wide areanetwork; an electronic converter module operable to receive and convertthe data representative of the weather conditions and transmitted by theplurality of weather monitoring stations into a format and protocol thatcan be processed and transmitted by the SCADA network; and an EAScomputing system communicably linked to said wide area network andadapted to receive data representative of the weather conditions astransmitted by the central server and adapted to present said datarepresentative of the weather conditions to a user interface for userviewing.